multivariate models

For this assignment will be analyzing data on the Vegetation and Environment in Dutch Dune Meadows.

To import the data and read the metadata run the following:

library(vegan)
data(dune)
data(dune.env)
?dune

1. Conduct an indirect ordination on the dune plant community. Specifically, visually examine a NMDS plot using the bray-curtis distance metric. Below is some code to help you develop a potential plot that emphasizes the role of the environmental variable “Moisture”. Describe how you interpret the graphic. What is the goal of creating such a plot? Does this analysis suggest any interesting findings with respect to the dune vegetation?

    plot(dune_mds, type='n')
    text(dune_mds, 'sp', cex=.5)
    # generate vector of colors 
    color_vect = rev(terrain.colors(6))[-1]
    points(dune_mds, 'sites', pch=19, 
           col=color_vect[dune.env$Moisture])
    legend('topright', paste("Moisture =", 1:5, sep=''), 
           col=color_vect, pch=19)
   

2. Carry out a direct ordination using CCA in order to test any potential hypotheses that you developed after examining the MDS plot. Specifically, carry out a test of the entire model (i.e., including all constrained axes) and also carry out tests at the scale of individual explanatory variables you included in your model if you included more than one variable. Interpret the tests and the overall fit of the model to your data. Plot and interpret your results.

3. Do your two analyses agree with one another or complement one another or do these two analyses seem to be suggesting different take home messages? Which analysis do you find to be more useful?