Spatial Variation in the Bacterial and Denitrifying Bacterial Community in a Biofilter

Spatial variation in the bacterial and denitrifying bacterial community in a biofilter

treating subsurface agricultural drainage

Supplemental material: This supplementary material file includes the lag size determination for the biofilter dataset (Figure S1), multidimensional scaling (MDS) visualizations of the bacterial community by depth (Figure S2), transect (Figure S3) and position (Figure S3), MDS visualizations of the denitrifying bacterial community by depth, transect, and position (Figure S4), an example of ANOSIM correlogram and significance plots for data with no spatial structure (Figure S5), and omnidirectional, flow path, and depth separated ANOSIM R and p plots for the denitrifying bacterial community (Figure

by J. Malia Andrus1, Matthew D. Porter2, Luis F. Rodriguez1, Timothy Kuehlhorn1,

Richard A. C. Cooke1, Yuanhui Zhang1, Angela D. Kent3, and Julie L. Zilles2*

1 Department of Agricultural and Biological Engineering, University of Illinois at

Urbana-Champaign, Urbana, IL

2 Department of Civil and Environmental Engineering, University of Illinois at

Urbana-Champaign, Urbana, IL

3 Department of Natural Resources and Environmental Sciences, University of

Illinois at Urbana-Champaign, Urbana, IL

* Corresponding author: 3230C Newmark Civil Engineering Laboratory, MC250, 205

North Mathews Avenue, Urbana, IL 61801, Phone (217) 244-2925, Fax (217) 333-6968,

E-mail: [email protected]

Selection of appropriate lag sizes

Successful visualization of spatial structure is reliant upon the choice of lag size and tolerance, because these parameters determine the distance classes. Lag size is the interval between distance classes, while lag tolerance is the width of the distance classes.

Lag size is typically selected initially based on the sampling scheme and then refined based on the data. For our test dataset, from a denitrifying biofilter, the sampling grid size is 2.74 m in the flowpath direction, 0.46 m in the cross-flowpath direction, and 0.76 m in the depth direction. ANOSIM correlograms were therefore produced for lag sizes of 0.46 m to 3.66 m (Figure S1). From these results we see the effect of sampling grid size on optimal lag size. For the smaller lag sizes, the number of pairs in each lag class varied substantially, resulting in differences in the significance of R. In contrast, the plot using lag size equaling the predominant grid size (2.74 m) is smooth and Gaussian-shaped with a sharp incline. The plot with a lag size of 3.66 m is also Gaussian-shaped, but fails to capture the spatial features as clearly. A lag size of 2.74 m was therefore used in all subsequent analysis. In this work the lag tolerance was set at half the lag size to include all data. SUPPLEMENTARY FIGURES:

Figure S1. Comparison of different lag classes in ANOSIM correlogram significance plots of the biofilter bacterial community as assessed through ARISA. Figure S2. Analysis of depth differences in bacterial community composition. Non- metric multidimensional Scaling (MDS) plots of bacterial community composition as assessed by ARISA. Each point represents microbial communities collected at either 0.76 m or 1.52 m. Figure S3. Variation in bacterial community composition by transect (A-C) and position

(D-F), as assessed by non-metric multidimensional scaling (MDS) on ARISA of each sample. Each point represents a microbial community sample collected at transects 1-12.

Bacterial communities exhibit spatial structure by transect at A) all depths combined, B)

0.76 m, and C) 1.52 m. No significant variation was observed in bacterial community composition by position, whether at all depths combined (D), 0.76 m depth (E), or 1.52 m depth (F). Figure S4. Denitrifying bacterial community composition, as illustrated with non-metric multidimensional scaling analysis of nosZ T-RFLP data. Each point represents a microbial community sample collected at A) either 0.76 m or 1.52 m depth, B) along transects 1-12, or C) at sampling positions A-C. Denitrifying bacteria did not show microbial community structure by A) depth, B) transect, or C) sampling position. Figure S5. Illustration of data without any spatial structure. ANOSIM R-plot (top) and significance plot (bottom) were generated from the same dataset used in Figure 2, but with random permutation of the sample locations to remove any existing spatial structure. Figure S6. ANOSIM correlogram R-plots and significance plots for denitrifying bacterial community samples. Left panel shows omnidirectional and flow direction plots, while the right panel shows samples separated by depth. None of the analyses indicated significant spatial structure. Solid symbols indicate data points with an R statistic significant to the

=0.05 level. Open symbols indicate data points with an R statistic that is not statistically significant. The dotted line shows the =0.05 significance threshold.