# Dictionaries 1

Some measures of similarity, like the Jaccard index in the Sets 1, are based only on the presence/absence of species. Another major class of similarity measures also includes information on their relative abundance in the community. One of these measures is the Euclidean distance (actually the Euclidean distance measures difference not similarity so the similarity measure is 1 - Euclidean distance), which is calculated as the ```sqrt(sum((N1_i - N2_i)^2))```, where `N1_i` is the relative abundance of species `i` at site 1 and `N2_i` is the relative abundance of species `i` at site 2 (including zeros). Relative abundance is the number of individuals of a species divided by the total abundance of all species at the site.

#### Data

Use the data from McGlinn et al. 2010. We need some information on the relative prevalence of the different species at the different sites so this time download the Cover table. Use the `cover` column as the measure of N (we often work with cover instead of number of individuals in plant communities). For this analysis we decide that instead of keeping the years in the analysis separate we want to combine the data from all of the years to get a longer time-scale picture.

#### Using dictionaries

Write a function that calculates the Euclidean distance for two sites when passed two dictionaries as arguments. Each dictionary should hold the information on the species identity of all species occurring at a site and the associated total cover of that species.

Write a series of commands that takes the imported data and creates one dictionary for each site that includes the species names and associated abundances for each site, where the abundance is the sum of all of the values in the cover column, for each species at the site. Then pass all possible pairs of dictionaries to your function for calculating the Euclidean distance between each pair of sites. Save the results to a csv file where the first column is the plot id for one of the two plots, the second column is the plot id for the other of the two plots, and the third column is the 1 minus the Euclidean Distance (our abundance based measure of similarity.