Bridging the Holistic-Reductionist Divide in Microbial Ecology

Abstract

Microbial communities are inherently complex systems. To address this complexity, microbial ecologists are developing new, more elaborate laboratory models at an ever-increasing pace. These model microbial communities and habitats have opened up the exploration of new territories that lie between the simplicity and controllability of "synthetic" systems and the convolution and complexity of natural environments. Here, we discuss this classic methodological divide, we propose a conceptual perspective that integrates new research developments, and we sketch a 3-point possible roadmap to cross the divide between controllability and complexity in microbial ecology.

Keywords: metagenomics; microbial communities; microbial diversity; synthetic ecology.

FIG 1

Dimensions of complexity in microbial communities. (A) We define three interdependent axes of complexity. The first spans genotypic diversity and abundance across all domains of life. The second axis encompasses environmental factors at all scales, including habitat structure, physicochemical gradients, and transport processes. The third axis deals with the system characteristics and its emergent properties, ranging from single biochemical reactions to ecosystem functions. All these elements can vary in space and time. Colored volumes exemplify representative types of microbial communities showing various levels of complexity, ranging from very high in all dimensions (soil microbial community) to very low in all dimensions (3-member consortium in batch culture). Other examples are natural microbial systems that show relatively low levels of complexity (microbial communities in the bee gut and from acid mine drainage) and hence represent intermediate research models. (B) Associated with the complexity dimensions are the levels of control and predictability of the microbial system, which are somewhat proportional to the system’s complexity (the relationship between control and predictability, however, may vary with microbial systems and with the type of predicted processes). Complex systems thus tend to be more amenable to descriptive investigations, while simplified ones tend to be more amenable to explanatory investigations. It is of course informative to study complex as well as simple systems. However, there is a trade-off between realism and interpretive power.