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Relative abundances of soil bacteria from 27 samples collected in nine forest and 18 grassland sites in Germany. The data set includes abundances of 18 bacterial phyla (including three candidate phyla) and five proteobacterial classes.
data(Bacteria)A data frame with 27 observations on the following 24 variables.
Land use typea factor with levels forest grassland
Acidobacteriaa numeric vector
Actinobacteriaa numeric vector
Bacteroidetesa numeric vector
Chloroflexia numeric vector
Cyanobacteriaa numeric vector
Deinococcus-Thermusa numeric vector
Fibrobacteresa numeric vector
Firmicutesa numeric vector
Fusobacteriaa numeric vector
Gemmatimonadetesa numeric vector
Nitrospiraa numeric vector
OP11a numeric vector
Planctomycetesa numeric vector
Spirochaetesa numeric vector
Tenericutesa numeric vector
TM7a numeric vector
Verrucomicrobiaa numeric vector
WS3a numeric vector
Alphaproteobacteriaa numeric vector
Betaproteobacteriaa numeric vector
Deltaproteobacteriaa numeric vector
Gammaproteobacteriaa numeric vector
Epsilonproteobacteriaa numeric vector
Relative abundances of 18 bacterial phyla (including three candidate phyla) and five proteobacterial classes (alpha, beta, gamma, delta and epsilon) from two ecological metagenomics studies (Will et al. 2010, Nacke et al. 2011). There are 27 observations altogether, nine of which stem from forest and 18 from grassland plots in Germany.
One goal of these investigations was to unravel differences in bacterial diversity and community composition between the land use types forest and grassland.
The bacteria's relative abundances were determined by analyzing the V2-V3 region of the 16S rRNA gene via pyrosequencing-based DNA techniques.
data(Bacteria)
str(Bacteria)
### Assess whether there is a difference in biodiversity and
### community composition species richness (Shannon index,
### Simpson index) between grassland and forest.
### Bootstrap times set to 50 due to example time settings
library(simboot)
mcpHill(dataf=Bacteria[,2:24], fact=Bacteria[,1], boots=50, qval=c(0,1,2))
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