Translating the ever-increasing wealth of information on microbiomes (environment, host, or built environment) to advance the understanding of system-level processes is proving to be an exceptional research challenge. One reason for this challenge is that relationships between characteristics of microbiomes and the system-level processes they influence are often evaluated in the absence of a robust conceptual framework and reported without elucidating the underlying causal mechanisms. The reliance on correlative approaches limits the potential to expand the inference of a single relationship to additional systems and advance the field. We propose that research focused on how microbiomes influence the systems they inhabit should work within a common framework and target known microbial processes that contribute to the system-level processes of interest. Here we identify three distinct categories of microbiome characteristics (microbial processes, microbial community properties, and microbial membership) and propose a framework to empirically link each of these categories to each other and the broader system level processes they affect. We posit that it is particularly important to distinguish microbial community properties that can be predicted from constituent taxa (community aggregated traits) from and those properties that are currently unable to be predicted from constituent taxa (emergent properties). Existing methods in microbial ecology can be applied to more explicitly elucidate properties within each of these categories and connect these three categories of microbial characteristics with each other. We view this proposed framework, gleaned from a breadth of research on environmental microbiomes and ecosystem processes, as a promising pathway with the potential to advance discovery and understanding across a broad range of microbiome science.
Ecological variables such as species richness (S) and total abundance (N) can strongly influence the forms of macroecological patterns. For example, the majority of variation in the species abundance distribution (SAD) can often be explained by the majority of possible forms having the same N and S, i.e. the feasible set. The feasible set reveals how variables such as N and S determine observable variation and whether empirical patterns are exceptional to the majority of possible forms. However, this approach has currently only been applied to the SAD using relatively inefficient random sampling algorithms. We extend the use of the feasible set approach by developing new algorithms to efficiently generate random samples of the feasible set for the SAD and the intraspecific spatial abundance distribution (SSAD). These algorithms are often several orders of magnitude faster than a previous method, which greatly increases the size and diversity of communities that can be examined.
An ecological theory of microbial biodiversity has yet to be developed. This shortcoming leaves patterns of abundance, distribution, and diversity for the most abundant and diverse organisms on Earth without a predictive framework. However, because of their high abundance and complex dynamics, microbial communities may be underpinned by lognormal dynamics, i.e., synergistic interactions among complex stochastic variables. Using a global-scale compilation of 20,456 sites from a diverse set of natural and host-related environments, we test whether a lognormal model predicts microbial distributions of abundance and diversity-abundance scaling laws better than other well-known models, including the most successful macroecological theory of biodiversity, i.e., maximum entropy theory of ecology. We found that the lognormal explains the greatest percent variation in abundance, that the success of the lognormal increased with abundance while other models decreased, and that the lognormal was the only model to reproduce recently documented diversity-abundance scaling laws. Our unifying ecological theory of microbial biodiversity explains and predicts macroecological patterns based on dynamics that capture the complex large number dynamics of microbial life.
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