#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_multiscale_analysis.r
#is the r-code for the multi-scale analysis. 
#This script can be used to generate the statistics for the multi-scale OLS pariwise regression analysis
#i.e. Table 2.

#input data
d100 =  read.csv(file = 'D:\\bci_publication\\r\\in\\TEMP\\d100.csv', header=T)
d50 = read.csv(file = 'D:\\bci_publication\\r\\in\\TEMP\\d50.csv', header=T)
d20 = read.csv(file = 'D:\\bci_publication\\r\\in\\TEMP\\d20.csv', header=T)

#base example
#var_d100=lm(sr_100~var, data = d100)
# number = subplot size
# var = remote sensing variable
#key
#sr = species richness
#stem = #of stems
#ba = basal area


#Correlation Matrices for each spatial scale
cor(d100) #1.0 ha (100 m ^2)
cor(d50) #0.25 ha (50 m ^2)
cor(d50) #0.04 ha (20 m ^2)

# All pairwise regression analysis are between species richness and each remote sensing predictor variable
#Repeated at each spatial scale.

#100 m subplots

#Plot variables
stem_100=lm(sr_100~stem_100, data = d100)
summary(stem_100)

ba_100=lm(sr_100~ba_100, data = d100)
summary(ba_100)

#Canopy structure variables
#mean canopy height
mch=lm(sr_100~mch, data = d100)
summary(mch)

mch_sd=lm(sr_100~mch_sd, data = d100)
summary(mch_sd)

#Quickbird Texture
qb_txt=lm(sr_100~qb_txt, data = d100)
summary(qb_txt)

qb_txt_sd=lm(sr_100~qb_txt_sd, data = d100)
summary(qb_txt_sd)

#Spectral Variables
evi=lm(sr_100~evi, data = d100)
summary(evi)

evi_sd=lm(sr_100~evi_sd, data = d100)
summary(evi_sd)

ndvi=lm(sr_100~ndvi, data = d100)
summary(ndvi)

ndvi_sd=lm(sr_100~ndvi_sd, data = d100)
summary(ndvi_sd)
	
rvi=lm(sr_100~rvi, data = d100)
summary(rvi)

rvi_sd=lm(sr_100~rvi_sd, data = d100)
summary(rvi_sd)

#Topographic variables
asp=lm(sr_100~asp, data = d100)
summary(asp)
	
asp_sd=lm(sr_100~asp_sd, data = d100)
summary(asp_sd)

curv=lm(sr_100~curv, data = d100)
summary(curv)

curv_sd=lm(sr_100~curv_sd, data = d100)
summary(curv_sd)

curv_plan=lm(sr_100~curv_plan, data = d100)
summary(curv_plan)	

curv_plan_sd=lm(sr_100~curv_plan_sd, data = d100)
summary(curv_plan_sd)

curv_pro=lm(sr_100~curv_pro, data = d100)
summary(curv_pro)
	
curv_pro_sd=lm(sr_100~curv_pro_sd, data = d100)
summary(curv_pro_sd)

dem=lm(sr_100~dem, data = d100)
summary(dem)

dem_sd=lm(sr_100~dem_sd, data = d100)
summary(dem_sd)	
	
twi=lm(sr_100~twi, data = d100)
summary(twi)

twi_sd=lm(sr_100~twi_sd, data = d100)
summary(twi_sd)

slp=lm(sr_100~slp, data = d100)
summary(slp)

slp_sd=lm(sr_100~slp_sd, data = d100)
summary(slp_sd)

#50 m subplots

stem_50=lm(sr_50~stem_50, data = d50)
summary(stem_50)

ba_50=lm(sr_50~ba_50, data = d50)
summary(ba_50)

mch=lm(sr_50~mch, data = d50)
summary(mch)

mch_sd=lm(sr_50~mch_sd, data = d50)
summary(mch_sd)

qb_txt=lm(sr_50~qb_txt, data = d50)
summary(qb_txt)

qb_txt_sd=lm(sr_50~qb_txt_sd, data = d50)
summary(qb_txt_sd)

evi=lm(sr_50~evi, data = d50)
summary(evi)

evi_sd=lm(sr_50~evi_sd, data = d50)
summary(evi_sd)

ndvi=lm(sr_50~ndvi, data = d50)
summary(ndvi)

ndvi_sd=lm(sr_50~ndvi_sd, data = d50)
summary(ndvi_sd)

rvi=lm(sr_50~rvi, data = d50)
summary(rvi)

rvi_sd=lm(sr_50~rvi_sd, data = d50)
summary(rvi_sd)

pc1_sd=lm(sr_50~pc1_sd, data = d50)
summary(pc1_sd)

asp=lm(sr_50~asp, data = d50)
summary(asp)

asp_sd=lm(sr_50~asp_sd, data = d50)
summary(asp_sd)

curv=lm(sr_50~curv, data = d50)
summary(curv)

curv_sd=lm(sr_50~curv_sd, data = d50)
summary(curv_sd)

curv_plan=lm(sr_50~curv_plan, data = d50)
summary(curv_plan)  

curv_plan_sd=lm(sr_50~curv_plan_sd, data = d50)
summary(curv_plan_sd)

curv_pro=lm(sr_50~curv_pro, data = d50)
summary(curv_pro)

curv_pro_sd=lm(sr_50~curv_pro_sd, data = d50)
summary(curv_pro_sd)

dem=lm(sr_50~dem, data = d50)
summary(dem)

dem_sd=lm(sr_50~dem_sd, data = d50)
summary(dem_sd)	

twi=lm(sr_50~twi, data = d50)
summary(twi)

twi_sd=lm(sr_50~twi_sd, data = d50)
summary(twi_sd)

slp=lm(sr_50~slp, data = d50)
summary(slp)

slp_sd=lm(sr_50~slp_sd, data = d50)
summary(slp_sd)

#20 m subplots

stem_20=lm(sr_20~stem_20, data = d20)
summary(stem_20)

ba_20=lm(sr_20~ba_20, data = d20)
summary(ba_20)

mch=lm(sr_20~mch, data = d20)
summary(mch)

mch_sd=lm(sr_20~mch_sd, data = d20)
summary(mch_sd)

qb_txt=lm(sr_20~qb_txt, data = d20)
summary(qb_txt)

qb_txt_sd=lm(sr_20~qb_txt_sd, data = d20)
summary(qb_txt_sd)

evi=lm(sr_20~evi, data = d20)
summary(evi)

evi_sd=lm(sr_20~evi_sd, data = d20)
summary(evi_sd)

ndvi=lm(sr_20~ndvi, data = d20)
summary(ndvi)

ndvi_sd=lm(sr_20~ndvi_sd, data = d20)
summary(ndvi_sd)

rvi=lm(sr_20~rvi, data = d20)
summary(rvi)

rvi_sd=lm(sr_20~rvi_sd, data = d20)
summary(rvi_sd)

pc1_sd=lm(sr_20~pc1_sd, data = d20)
summary(pc1_sd)

asp=lm(sr_20~asp, data = d20)
summary(asp)

asp_sd=lm(sr_20~asp_sd, data = d20)
summary(asp_sd)

curv=lm(sr_20~curv, data = d20)
summary(curv)

curv_sd=lm(sr_20~curv_sd, data = d20)
summary(curv_sd)

curv_plan=lm(sr_20~curv_plan, data = d20)
summary(curv_plan)  

curv_plan_sd=lm(sr_20~curv_plan_sd, data = d20)
summary(curv_plan_sd)

curv_pro=lm(sr_20~curv_pro, data = d20)
summary(curv_pro)

curv_pro_sd=lm(sr_20~curv_pro_sd, data = d20)
summary(curv_pro_sd)

dem=lm(sr_20~dem, data = d20)
summary(dem)

dem_sd=lm(sr_20~dem_sd, data = d20)
summary(dem_sd)	

twi=lm(sr_20~twi, data = d20)
summary(twi)

twi_sd=lm(sr_20~twi_sd, data = d20)
summary(twi_sd)

slp=lm(sr_20~slp, data = d20)
summary(slp)

slp_sd=lm(sr_20~slp_sd, data = d20)
summary(slp_sd)

#END