diff --git a/code/04_buildHeader.Rmd b/code/04_buildHeader.Rmd
index 3aae24579880c85674d835ea7e272cc011cceced..ad7b3f80f49a19e2bb083b58b600a9d6266eab7e 100644
--- a/code/04_buildHeader.Rmd
+++ b/code/04_buildHeader.Rmd
@@ -151,7 +151,8 @@ Pfe-f-08 - 1707849
 Pfe-o-05- 1707854  
 ```{r, eval=T}
 header0 <- header0 %>% 
-  filter(!PlotObservationID %in% c(1707776, 1707779:1707782, 1707849, 1707854))
+  filter(!PlotObservationID %in% c(1707776, 1707779:1707782, 1707849, 1707854)) %>% 
+  filter(Dataset != "$Coastal_Borja")
 ```
 
 
@@ -394,7 +395,7 @@ header <- header %>%
             by="PlotObservationID") %>% 
   mutate(CONTINENT=factor(continent, 
                             levels=c("Africa", "Antarctica", "Australia", "Eurasia", "North America", "South America"),
-                            labels=c("AF", "AN", "AU", "EU", "NA", "SA"))) %>% 
+                            labels=c("AF", "AN", "AU", "EU", "N-A", "S-A"))) %>% 
   
   dplyr::select(-continent)
 ```
diff --git a/code/05_ExtractEnvironment.Rmd b/code/05_ExtractEnvironment.Rmd
index 3038aca705f4f01e361e3bdd22cee7184b9a70d0..3bea535f029aa3a8c3b753fd1d45ee7c2ad4af97 100644
--- a/code/05_ExtractEnvironment.Rmd
+++ b/code/05_ExtractEnvironment.Rmd
@@ -295,7 +295,8 @@ elevation.out <- read_csv("../_derived/elevatr/elevation.out.csv")
 soilclim <- header %>% 
   dplyr::select(PlotObservationID) %>% 
   left_join(elevation.out %>% 
-              dplyr::select(PlotObservationID, Elevation_median, Elevation_q2.5, Elevation_q97.5, Elevation_DEM.res=DEM.res), 
+              dplyr::select(PlotObservationID, Elevation_median, Elevation_q2.5, Elevation_q97.5, Elevation_DEM.res=DEM.res) %>% 
+              filter(!is.na(Elevation_median)), 
             by="PlotObservationID") %>% 
   left_join(header.shp@data %>% 
               dplyr::select(PlotObservationID) %>% 
@@ -305,6 +306,8 @@ soilclim <- header %>%
               bind_cols(isric.sd.out) %>% 
               distinct(), 
             by="PlotObservationID")
+dim(soilclim)
+nrow(soilclim)==nrow(header)
 ```
 
 ```{r, echo=F}
diff --git a/code/07_buildCWMs.Rmd b/code/07_buildCWMs.Rmd
index 0e128cc4199df289b873f0cf526107ffd91d3375..99c0eb6063effc18776365b323c204f0b9733863 100644
--- a/code/07_buildCWMs.Rmd
+++ b/code/07_buildCWMs.Rmd
@@ -15,7 +15,7 @@ output: html_document
 **Revised:**  
 **version:** 1.0
   
-This reports documents 1) the construction of Community Weighted Means (CWMs) and Variance (CWVs); and 2) the classification of plots into forest\\non-forest based on species growth forms. It complements species composition data from sPlot 3.0 and gap-filled plant functional traits from TRY 5.0, as received by [Jens Kattge](jkattge@bgc-jena.mpg.de) on Jan 21, 2020. 
+This report documents 1) the construction of Community Weighted Means (CWMs) and Variance (CWVs); and 2) the classification of plots into forest\\non-forest based on species growth forms. It complements species composition data from sPlot 3.0 and gap-filled plant functional traits from TRY 5.0, as received by [Jens Kattge](jkattge@bgc-jena.mpg.de) on Jan 21, 2020. 
 
 *Changes in version 1.1* - Standardized Growth form names in sPlot_traits.  
 
@@ -202,7 +202,7 @@ This results in the exclusion of `r length(unique(c(toexclude, toexclude2, toexc
 
 
 ## 1.4 Calculate species and genus level trait means and sd
-```{r}
+```{r, cache=T,  cache.lazy=F, warning=F}
 ## Calculate species level trait means and sd. 
 try.species.means <- try.individuals %>% 
   group_by(Name_short) %>% 
@@ -335,7 +335,10 @@ CWM0 <- DT2.comb %>%
               dplyr::rename(Species=Taxon_name) %>% 
               dplyr::select(Species, Rank_correct, ends_with("_mean")), 
             by=c("Species", "Rank_correct"))
+```
 
+
+```{r, cache=T,  cache.lazy=F, warning=F}
 # Calculate CWM for each trait in each plot
 CWM1 <- CWM0 %>%
   group_by(PlotObservationID) %>%
@@ -343,7 +346,10 @@ CWM1 <- CWM0 %>%
                .funs = list(~weighted.mean(., Relative_cover, na.rm=T))) %>%
   dplyr::select(PlotObservationID, order(colnames(.))) %>%
   gather(key=variable, value=CWM, -PlotObservationID)
+```
+
 
+```{r, cache=T,  cache.lazy=F, warning=F}
 # Calculate coverage for each trait in each plot
 CWM2 <- CWM0 %>%
   mutate_at(.funs = list(~if_else(is.na(.),0,1) * Relative_cover), 
@@ -353,7 +359,10 @@ CWM2 <- CWM0 %>%
                .funs = list(~sum(., na.rm=T))) %>%
   dplyr::select(PlotObservationID, order(colnames(.))) %>%
   gather(key=variable, value=trait.coverage, -PlotObservationID)
-  
+```
+
+
+```{r, cache=T,  cache.lazy=F, warning=F}
 # Calculate CWV
 # Ancillary function
 variance2.fun <- function(trait, abu){
@@ -377,7 +386,10 @@ CWM3 <- CWM0 %>%
                .funs = list(~variance2.fun(., Relative_cover))) %>%
   dplyr::select(PlotObservationID, order(colnames(.))) %>%
   gather(key=variable, value=CWV, -PlotObservationID)
+```
+
 
+```{r, cache=T,  cache.lazy=F, warning=F}
 ## Calculate proportion of species having traits
 CWM4 <- CWM0 %>%
   group_by(PlotObservationID) %>%
@@ -479,12 +491,6 @@ knitr::kable(CWM.summary,
                   full_width = F, position = "center")
 ```
 
-### 2.2 Export CWM and species mean trait values
-```{r}
-save(try.combined.means, CWM, file="../_output/Traits_CWMs_sPlot3.RData")
-```
-
-
 # 3 Classify plots in `is.forest` or `is.non.forest` based on species traits
 sPlot has two independent systems for classifying plots to vegetation types. The first relies on the expert opinion of data contributors and classifies plots into broad habitat types. These broad habitat types are coded using 5, non-mutually exclusive dummy variables:  
 1) Forest