377.vegetation.lab.doc - BIO 377 Lab Exercise Vegetation...

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BIO 377 Lab Exercise: Vegetation Data and Diversity Goals: Understand the patterns of diversity and species associations across the environmental gradient of the Manu Tree community data set. Specifically: (1) Understand measures of diversity and community similarity. (2) Calculate similarity indices for the Manu Tree plots. (3) Understand ordination based on similarity of occurrence and abundance. Also, you should understand how the number of species sampled changes with sample size. To do this we will look at: (1) Species-area and species-individual curves (2) Rarefaction I NTRODUCTION : How similar are two communities? The question seems simple, but there are lots of ways that communities can vary, e.g. (1) Species richness (2) Species diversity (3) Compositional similarity S PECIES RICHNESS ( SORTING , CONSOLIDATING ) Q: How many species are in the sample? How do we compare among samples of different sizes?
Today we’ll be using the R package “vegan” for community analyses. Open R and load the package “vegan” from the “packages” menu. Also, load “MASS.” To use the package the data have to be in the form of a community matrix. The basic form is to have the species as columns and the plots as rows. The package vegan has several example data sets. One of the examples comes from the famous 50 hectare plot on Barro Colorado Island, Panama. The data set has 50 rows, one for each of the hectares in the plot, and one column for each of the 225 species found there. The data are numbers of individuals >10cm dbh. We’ll read a lot of primary literature based on this plot, and you have the data to play with. Paste the following into R: data(BCI) #loads BCI data dim(BCI) #gives you the dimensions of the data set, (rows, columns) BCI[1:10,20:25] #shows the data for rows 1:10 and columns 20:25 If you can’t see everything at once, scroll up: R records the output. Now, look at the first 5 rows of columns 70:75.

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