#Supplement 1

# Fricker, G.A., Wolf, J.A., Saatchi, S.S., Gillespie, T.W. 
#Predicting spatial variations of tree species richness in tropical forests from high resolution remote sensing. Ecological Applications.
# File name: 2015_02_03_bci_main.r
#is the r-code for reading in all data files and creating correlation matrices.  
#Also contains the base linear model used for the OLS regression models.  

#The Input tables are the plot and remote sensing statistics for each subplot in the 50-ha plot at the 1-ha scale
#Each row is a subplot with the geographic location of each plot center (utm 17N m)

#Plot variables include species richness (sr), stems (stem) and basal area (ba).  
#Remote Sensing predictory variables are listed in the readme document

#Main tables for the OLS pairwise regression analysis for each size class
#change all directory paths
d200 = read.csv(file = 'D:\\bci_publication\\r\\in\\gls\\200.csv', header=T)
d100 = read.csv(file = 'D:\\bci_publication\\r\\in\\gls\\100.csv', header=T)
d10 = read.csv(file = 'D:\\bci_publication\\r\\in\\gls\\10.csv', header=T)
d10_100 = read.csv(file = 'D:\\bci_publication\\r\\in\\gls\\10_100.csv', header=T)
d10_200 = read.csv(file = 'D:\\bci_publication\\r\\in\\gls\\10_200.csv', header=T)

#Pairwise correlation matrices for OLS regression
cor(d10_100)
cor(d10_200) #excluded from final manuscript, but included as a reference.
cor(d10)
cor(d100)
cor(d200)

#Each remote sensing variables can be analyzed individually by computing a linear model

#example  #linear regression between the species richness amongst all stems and the TWI (sd)
#variables can be changed as necessary

linear_model = lm(sr_all~twi_sd, data=d10)  
summary(linear_model)