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Diversity and dynamics of rare and of resident bacterial populations in coastal sands

A walkthrough based on...

Gobet A, Böer SI, Huse SM, van Beusekom JEE, Quince C, Sogin ML, Boetius A, Ramette A.(2012) Diversity and dynamics of rare and of resident bacterial populations in coastal sandsISME J. 6: 542–553.

The walkthrough below illustrates the central procedures of an investigation conducted by Gobet et al. (2012). These authors assessed the diversity and dynamics of bacterial populations in coastal sands. In their study, sediment cores were taken at a georeferenced location every two to three months from February 2005 to March 2006. Three layers of each core were then processed similarly. Note that porewater and seawater samples were also processed in the study, but, in aid of brevity, are not shown in this walkthrough.

Following sample acquisition, relative abundance data of bacterial taxa were determined by sequencing the hypervariable V6 region of the 16S rRNA gene with 454 massively-parallel tag sequencing. Contextual parameters were measured and included pigment concentrations, enzyme activities, and nutrient concentrations.

Data preparation

Prior to analysis, raw sequences were binned as operational taxonomic units (OTUs) and denoised. In analogy to the classical "sites × species" data table, OTUs serve as the variables in a "samples × OTU" data table. OTU relative abundances were Hellinger transformed to help meet the assumptions of redundancy analysis (RDA) and related methods. Selected contextual variables were log transformed prior to RDA.

Multiple linear regression (MLR) was used to test for multicollinearity among the explanatory variables."Time" and "depth" were treated as response variables in the MLR model  to determine if these were confounded with other contextual parameters. They were then deliberately included or removed from subsequent analyses as deemed appropriate.

Assessing changes in local diversity (alpha diversity)

The denoised OTU abundance tables were used to assess the alpha diversity present in each sample. OTU richness and evenness were visualised using three-dimensional barplots and pie charts and richness was reported in terms of OTU numbers per sample or other relevant division.

Comparing changes in between-sample diversity (beta diversity)

A general increase of diversity with sediment depth was observed and a high turnover of OTUs (i.e. very different OTU composition) was reported across time and between depth layers. The percentage of shared OTUs between samples was reported as a measure of beta diversity

Disentangling the effects of factors influencing community structure

Redundancy analysis (RDA), a form of constrained analysis, was used to assess the influence of contextual explanatory variables on the OTU abundance data. A stepwise forward selection procedure (see "Variable selection in MLR" under multiple linear regression) was used to select variables which "best" explained variation in the abundance data, with significance assessed by permutation. The final model, including only selected explanatory contextual variables, was the final result of this stage of the analysis. Given this reduced model, variation partitioning (VP) using partial RDA was then used to determine how much variation in the OTU abundance data was attributable to each selected contextual parameter, controlling for the influence of the others. 

Examining individual OTU relationships to contextual parameters

Pairwise univariate Pearson correlation analysis was conducted between OTU data aggregated at different taxonomic levels and contextual variables. Correction for multiple testing was employed to control the Type I error rate.

The illustration below is a simplified representation of the procedures used by Gobet et al. Please consult the manuscript for a more complete explanation.