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Microbial biofilms are ubiquitous in aquatic environments where they provide important ecosystem functions. A key property believed to influence the community structure and function of biofilms is thickness. However, since biofilm thickness is inextricably linked to external factors such as water flow, temperature, development age and nutrient conditions, its importance is difficult to quantify. Here, we designed an experimental system in a wastewater treatment plant whereby nitrifying biofilms with different thicknesses (50 or 400 µm) were grown in a single reactor, and thus subjected to identical external conditions. The 50 and 400 µm biofilm communities were significantly different. This beta-diversity between biofilms of different thickness was primarily caused by deterministic factors. Turnover (species replacement) contributed more than nestedness (species loss) to the beta-diversity, i.e. the 50 µm communities were not simply a subset of the 400 µm communities. Moreover, the two communities differed in the composition of nitrogen-transforming bacteria and in nitrogen transformation rates. The study illustrates that biofilm thickness alone is a key driver for community composition and ecosystem function, which has implications for biotechnological applications and for our general understanding of biofilm ecology.

Background High-throughput amplicon sequencing of marker genes, such as the 16S rRNA gene in Bacteria and Archaea, provides a wealth of information about the composition of microbial communities. To quantify differences between samples and draw conclusions about factors affecting community assembly, dissimilarity indices are typically used. However, results are subject to several biases, and data interpretation can be challenging. The Jaccard and Bray-Curtis indices, which are often used to quantify taxonomic dissimilarity, are not necessarily the most logical choices. Instead, we argue that Hill-based indices, which make it possible to systematically investigate the impact of relative abundance on dissimilarity, should be used for robust analysis of data. In combination with a null model, mechanisms of microbial community assembly can be analyzed. Here, we also introduce a new software, qdiv, which enables rapid calculations of Hill-based dissimilarity indices in combination with null models. Results Using amplicon sequencing data from two experimental systems, aerobic granular sludge (AGS) reactors and microbial fuel cells (MFC), we show that the choice of dissimilarity index can have considerable impact on results and conclusions. High dissimilarity between replicates because of random sampling effects make incidence-based indices less suited for identifying differences between groups of samples. Determining a consensus table based on count tables generated with different bioinformatic pipelines reduced the number of low-abundant, potentially spurious amplicon sequence variants (ASVs) in the data sets, which led to lower dissimilarity between replicates. Analysis with a combination of Hill-based indices and a null model allowed us to show that different ecological mechanisms acted on different fractions of the microbial communities in the experimental systems. Conclusions Hill-based indices provide a rational framework for analysis of dissimilarity between microbial community samples. In combination with a null model, the effects of deterministic and stochastic community assembly factors on taxa of different relative abundances can be systematically investigated. Calculations of Hill-based dissimilarity indices in combination with a null model can be done in qdiv, which is freely available as a Python package (https://github.com/omvatten/qdiv). In qdiv, a consensus table can also be determined from several count tables generated with different bioinformatic pipelines.

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