diff --git a/merge.data.BCI.R b/merge.data.BCI.R
index f6c2a47c1a05a62071a599b251acf4b4c8dbbbe1..87d3b7787d51edfb7de6b4a0727a3aed44c7ac34 100644
--- a/merge.data.BCI.R
+++ b/merge.data.BCI.R
@@ -21,7 +21,7 @@ for(k in 2:7) {
 			sort = T, by = "TreeID") }
 	data.bci1 <- data.bci2 
 	cat("Census", k, "now included\n") }
-rm(data.bci1, data.bci2, data.bci3, sub.bci)
+rm(data.bci1, data.bci2, sub.bci)
 big.bci <- big.bci[order(big.bci$TreeID),]; colnames(big.bci)[c(7:8)] <- c("DBH1","Date1")
 data.bci <- big.bci; rm(big.bci)
 
@@ -39,8 +39,12 @@ length(unique(data.bci$sp))
 ######################################
 ## MASSAGE TRAIT DATA
 ############################
-## THERE ARE HEIGHT VARAIBLES IN THE TRAITS DATA, BUT WE WILL NEED TO DISCUSS WHICH ONES TO USE
+## Use HEIGHT_AVG, LMALAM_AVD, SEED_DRY, BUT I DO NOT KNOW WHICH WOOD DENSITY VARIABLE TO USE
+data.trait <- read.csv("./data/raw/DataBCI/BCITRAITS_20101220.csv",stringsAsFactors=FALSE, header = T)
+data.trait$Latin <- apply(data.trait[,1:2], 1, paste, collapse = " ")
+data.bci <- merge(data.bci, data.trait[,c(ncol(data.trait),3,7:10,13,15,18,20:21)], by = "Latin", all.x = T)
 
+data.bci <- data.bci[order(data.bci$TreeID),]
 ##########################################
 ## FORMAT INDIVIDUAL TREE DATA
 #############
@@ -53,13 +57,13 @@ data.bci$year <- as.numeric(difftime(data.bci$Date2, data.bci$Date1, units = "we
 data.bci$G <- 10*(data.bci$DBH1-data.bci$DBH1)/data.bci$year ## diameter growth in mm per year - BASED ON UNROUNDED YEARS
 data.bci$D <- data.bci[["DBH1"]]; 
 data.bci$plot <- data.bci[["Quadrat"]] ## plot code?
-data.bci$htot <- rep(NA,length(data.bci[["G"]])) ## DOES IT COME FROM THE TRAITS DATA?
+data.bci$htot <- data.bci$HEIGHT_AVG
 data.bci$sp.name <- data.bci$Latin
 
 ######################
 ## ECOREGION
 ###################
-## bci has only 1 eco-region?
+## bci has only 1 eco-region
 
 ######################
 ## PERCENT DEAD
@@ -76,7 +80,6 @@ data.bci <- merge(data.bci,data.frame(plot=names(perc.dead),perc.dead=perc.dead)
 ###################
 ## Remove data with dead == 1
 table(data.bci$dead)
-## Nothing to remove
 
 vec.abio.var.names <-  c("MAT","MAP") ## MISSING 
 vec.basic.var <-  c("treeid","sp","sp.name","plot","D","G","dead","year","htot","x","y","perc.dead")