Introduction
Theory, Examples & Exercises
Section: Ordination analysis
(This exercise is based on Case study 4 from Šmilauer & Lepš 2014, and the coding was partly inspired by the blog post of Gavin Simpson, analysing this data - I recommend it for checking on more details.)
Use vegetation data from an experimental study on wet meadow, which tried to answer the question how is abundance and species composition of seedlings of herb species on wet meadow influenced by experimental removal of dominant clonal plant species (Nardus stricta), plant litter (remnants of plant biomass from the previous season in case that the meadow was not mown) and removal of litter+mosses (by raking). Before analysis, please get familiar with the data you will be using. Data are from completely randomized block design with four blocks, each with four plots (one per treatment, including control). The block design will need to be taken into consideration when testing the significance of explained variation using Monte Carlo permutation test.
seedlings.spe
and seedlings.env
- the first containing species composition of seedlings per plot, the second contain assignment of the plot to the block and experimental treatment.seedlings.env$blocks
as covariable in the model.For hints click here ☛
For hints click here ☛
read.delim
; decorana
results. If over 4, use CCA, if less then 3, use RDA, 3-4 -both are fine;seedlings.spe ~ treatment + Condition (block), data = seedlings.env
;ordiplot
anova
without any other arguments; since the blocks
are defined as covariable, he will test the significance of variation after removal of their variation;anova
with argument by = “axis”
.