Prepared ALSO data based on species covers

00_Mesobromion_DataPreparation.R

@@ -68,13 +68,15 @@ alltry <- TRY.all.mean.sd.3.by.genus.species.tree %>%
   dplyr::select(-Wood.vessel.length.mean, -StemDens.mean, -Stem.cond.dens.mean) %>% 
   rename_all(.funs=~gsub(pattern=".mean$", replacement="", x=.))
 
-traits <- traits0 %>% 
+all.traits <- traits0 %>% 
   ungroup() %>% 
   #dplyr::select(species, species0) %>% 
   left_join(alltry %>% 
               rename(species=StandSpeciesName), 
-            by="species") %>% 
+            by="species") 
+traits <- all.traits %>% 
   filter(!is.na(LeafArea))
+dim(all.traits) #[1] 907  81
 dim(traits) #[1] 805  2
 
 
@@ -159,71 +161,76 @@ traits <- traits %>%
   filter(species0 %in% colnames(species))
 
 
-#recode binary traits to nominal
-colnames(traits)[which(colnames(traits)=="LBE_D_plurienn_hapaxanth")] <- "LEB_D_plurienn_hapaxanth"
-traits <- traits %>% 
-  mutate(BLU_KL_NEKTAR_HONIG_INSEKTEN=replace(BLU_KL_NEKTAR_HONIG_INSEKTEN, 
-                                              list=species0 %in% c("Convallaria_majalis", "Maianthemum_bifolium"),
-                                              values=0))
-
-traits <- traits %>% 
-  as.tbl() %>% 
-  dplyr::select(-starts_with("BL_FORM"), -starts_with("REPR_T"), -starts_with("BLU_KL"), -starts_with("STRAT_T"), -starts_with("BL_AUSD")) %>% 
-  left_join(traits %>% 
-              dplyr::select(species0, `BL_AUSD_immergrün`:`BL_AUSD_überwinternd_grün`, REPR_T_Samen_Sporen:STRAT_T_SR) %>% 
-              gather(key=Trait, value="value", -species0) %>% 
-              separate(Trait, into = c("Trait", "Organ", "Level"), sep = "_", extra = "merge") %>% 
-              unite(Trait, Trait, Organ) %>% 
-              filter(value==1) %>% 
-              dplyr::select(-value) %>% 
-              spread(Trait, Level) %>% 
-              mutate_at(.vars=vars(BL_AUSD:STRAT_T), 
-                        .funs=~as.factor(.)), 
-            by="species0")
-
-## recode traits to numeric
-robust.mean <- function(x1,x2=NA,x3=NA,x4=NA){
-  x <- c(x1,x2,x3,x4)
-  if(any(!is.na(x))){mean(x, na.rm=T)} else {NA}
+recode.traits <- function(x){
+  ## recode traits to numeric
+  #recode binary traits to nominal
+  
+  robust.mean <- function(x1,x2=NA,x3=NA,x4=NA){
+    x <- c(x1,x2,x3,x4)
+    if(any(!is.na(x))){mean(x, na.rm=T)} else {NA}
+  }
+  colnames(x)[which(colnames(x)=="LBE_D_plurienn_hapaxanth")] <- "LEB_D_plurienn_hapaxanth"
+  x <- x %>% 
+    mutate(BLU_KL_NEKTAR_HONIG_INSEKTEN=replace(BLU_KL_NEKTAR_HONIG_INSEKTEN, 
+                                                list=species0 %in% c("Convallaria_majalis", "Maianthemum_bifolium"),
+                                                values=0))
+  
+  x <- x %>% 
+    as.tbl() %>% 
+    dplyr::select(-starts_with("BL_FORM"), -starts_with("REPR_T"), -starts_with("BLU_KL"), -starts_with("STRAT_T"), -starts_with("BL_AUSD")) %>% 
+    left_join(x %>% 
+                dplyr::select(species0, `BL_AUSD_immergrün`:`BL_AUSD_überwinternd_grün`, REPR_T_Samen_Sporen:STRAT_T_SR) %>% 
+                gather(key=Trait, value="value", -species0) %>% 
+                separate(Trait, into = c("Trait", "Organ", "Level"), sep = "_", extra = "merge") %>% 
+                unite(Trait, Trait, Organ) %>% 
+                filter(value==1) %>% 
+                dplyr::select(-value) %>% 
+                spread(Trait, Level) %>% 
+                mutate_at(.vars=vars(BL_AUSD:STRAT_T), 
+                          .funs=~as.factor(.)), 
+              by="species0")
+  
+  out <- x %>% 
+    dplyr::select(-starts_with("BL_ANAT"), -starts_with("LEB_D"), -starts_with("ROS_T")) %>% 
+    left_join(x %>% 
+                dplyr::select(species0, starts_with("BL_ANAT")) %>% 
+                mutate(BL_ANAT_helomorph=ifelse(BL_ANAT_helomorph==1, 1, NA)) %>% 
+                mutate(BL_ANAT_hygromorph=ifelse(BL_ANAT_hygromorph==1, 2, NA)) %>% 
+                mutate(BL_ANAT_mesomorph=ifelse(BL_ANAT_mesomorph==1, 3, NA)) %>% 
+                mutate(BL_ANAT_skleromorph=ifelse(BL_ANAT_skleromorph==1, 4, NA)) %>% 
+                rowwise() %>% 
+                mutate(BL_ANAT=robust.mean(BL_ANAT_helomorph, BL_ANAT_hygromorph, BL_ANAT_mesomorph, BL_ANAT_skleromorph)) %>% 
+                ungroup() %>% 
+                dplyr::select(species0, BL_ANAT, BL_ANAT_blattsukkulent), 
+              by="species0") %>% 
+    left_join(x %>% 
+                dplyr::select(species0, starts_with("LEB_D"))  %>%
+                rowwise() %>% 
+                mutate(LEB_D_plurienn=max(LEB_D_plurienn_pollakanth + LEB_D_plurienn_hapaxanth, na.rm=T)) %>% 
+                ungroup() %>% 
+                mutate(LEB_D_plurienn=ifelse(LEB_D_plurienn==1, 3, NA)) %>% 
+                mutate(LEB_D_annuell=ifelse(LEB_D_annuell==1, 1, NA)) %>% 
+                mutate(LEB_D_bienn =ifelse(LEB_D_bienn==1, 2, NA)) %>% 
+                rowwise() %>% 
+                mutate(LEB_D=robust.mean(LEB_D_annuell, LEB_D_bienn, LEB_D_plurienn)) %>% 
+                ungroup() %>% 
+                dplyr::select(species0, LEB_D), 
+              by="species0") %>% 
+    left_join(x %>% 
+                dplyr::select(species0, starts_with("ROS_T")) %>% 
+                mutate(ROS_T=ROS_T_Ganzrosettenpflanzen) %>% 
+                mutate(ROS_T=replace(ROS_T, 
+                                     list=ROS_T_Halbrosettenpflanze==1, 
+                                     values=0.5)) %>% 
+                mutate(ROS_T=replace(ROS_T, 
+                                     list=ROS_T_rosettenlose.Pflanzen==1, 
+                                     values=0)) %>% 
+                dplyr::select(species0, ROS_T), 
+              by="species0") 
+  return(out)
 }
-
-traits <- traits %>% 
-  dplyr::select(-starts_with("BL_ANAT"), -starts_with("LEB_D"), -starts_with("ROS_T")) %>% 
-  left_join(traits %>% 
-              dplyr::select(species0, starts_with("BL_ANAT")) %>% 
-              mutate(BL_ANAT_helomorph=ifelse(BL_ANAT_helomorph==1, 1, NA)) %>% 
-              mutate(BL_ANAT_hygromorph=ifelse(BL_ANAT_hygromorph==1, 2, NA)) %>% 
-              mutate(BL_ANAT_mesomorph=ifelse(BL_ANAT_mesomorph==1, 3, NA)) %>% 
-              mutate(BL_ANAT_skleromorph=ifelse(BL_ANAT_skleromorph==1, 4, NA)) %>% 
-              rowwise() %>% 
-              mutate(BL_ANAT=robust.mean(BL_ANAT_helomorph, BL_ANAT_hygromorph, BL_ANAT_mesomorph, BL_ANAT_skleromorph)) %>% 
-              ungroup() %>% 
-              dplyr::select(species0, BL_ANAT, BL_ANAT_blattsukkulent), 
-            by="species0") %>% 
-  left_join(traits %>% 
-              dplyr::select(species0, starts_with("LEB_D"))  %>%
-              rowwise() %>% 
-              mutate(LEB_D_plurienn=max(LEB_D_plurienn_pollakanth + LEB_D_plurienn_hapaxanth, na.rm=T)) %>% 
-              ungroup() %>% 
-              mutate(LEB_D_plurienn=ifelse(LEB_D_plurienn==1, 3, NA)) %>% 
-              mutate(LEB_D_annuell=ifelse(LEB_D_annuell==1, 1, NA)) %>% 
-              mutate(LEB_D_bienn =ifelse(LEB_D_bienn==1, 2, NA)) %>% 
-              rowwise() %>% 
-              mutate(LEB_D=robust.mean(LEB_D_annuell, LEB_D_bienn, LEB_D_plurienn)) %>% 
-              ungroup() %>% 
-              dplyr::select(species0, LEB_D), 
-            by="species0") %>% 
-  left_join(traits %>% 
-              dplyr::select(species0, starts_with("ROS_T")) %>% 
-              mutate(ROS_T=ROS_T_Ganzrosettenpflanzen) %>% 
-              mutate(ROS_T=replace(ROS_T, 
-                                   list=ROS_T_Halbrosettenpflanze==1, 
-                                   values=0.5)) %>% 
-              mutate(ROS_T=replace(ROS_T, 
-                                   list=ROS_T_rosettenlose.Pflanzen==1, 
-                                   values=0)) %>% 
-              dplyr::select(species0, ROS_T), 
-            by="species0") 
+traits <- recode.traits(traits)
+ 
 
 ### ordered factors
 
@@ -279,8 +286,9 @@ env <- env %>%
 
 ##export for Valerio
 write_delim(species, path="_data/Mesobromion/species.out.10perc.txt", delim="\t")
-write_delim(traits, path="_data/Mesobromion/traits.out.10perc.txt", delim="\t")
-write_delim(env, path="_data/Mesobromion/env.10perc.txt", delim="\t")
+write_delim(traits, path="_data/Mesobromion/traits.out.10perc.cov.txt", delim="\t")
+write_delim(env, path="_data/Mesobromion/env.10perc.cov.txt", delim="\t")
+
 
 ## version without missing species
 empty <- which(colSums(species[,-1])==0)
@@ -290,13 +298,54 @@ species_nozero <- species[,-(empty+1)]
 write_delim(species_nozero , path="_data/Mesobromion/species.out.10perc_nozero.txt", delim="\t")
 write_delim(traits_nozero, path="_data/Mesobromion/traits.out.10perc_nozero.txt", delim="\t")
 
-
 write_delim(species %>% 
               dplyr::select(RELEVE_NR), 
             path="_derived/Mesobromion/ReleveList.txt", delim="\t")
 
-
-
+### version with cover values ### 4/08/2020
+species.proz <- read_csv("_data/Mesobromion/GVRD_Mes2_proz.csv", locale = locale(encoding = 'latin1'))
+species.proz$RELEVE_NR <- env0$RELEVE_NR
+species.proz <- species.proz %>% 
+  filter(RELEVE_NR %in% (species %>% pull(RELEVE_NR))) %>% 
+  #transform percentage cover to relative.cover
+  mutate(sumVar = rowSums(.[-1])) %>% 
+  mutate_at(.vars=vars(-RELEVE_NR), 
+            .funs=~./sumVar) %>% 
+  dplyr::select(-sumVar) %>% 
+  ## delete species not appearing in any plot
+  dplyr::select(colnames(.)[which(colSums(.)!=0)])
+dim(species.proz) #[1] 558 533
+write_delim(species.proz , path="_data/Mesobromion/species.out.10perc.cov.txt", delim="\t")
+
+## align traits to species in species.proz
+traits.proz <- recode.traits(all.traits)
+traits.proz <- data.frame(species=colnames(species.proz)[-1] ) %>% 
+  ### clean species names in both data.frames
+  mutate(species0=as.character(species)) %>% 
+  rowwise() %>% 
+  # quick and dirty clean up names
+  mutate(species=gsub(pattern="_agg_|_x_|_spec$|_agg$|_s_|_Sec_|__", replacement="_", x=species)) %>% 
+  mutate(species=gsub(pattern="_$", replacement = "", x = species)) %>% 
+  mutate(species=ifelse(is.na(word(species, 1, 2)), species, word(species, 1, 2))) %>% 
+  ungroup() %>% 
+  left_join(traits.proz %>% 
+              mutate(species=species0) %>% 
+              rowwise() %>% 
+              mutate(species=gsub(pattern="_agg_|_x_|_spec$|_agg$|_s_|_Sec_|__", replacement="_", x=species)) %>% 
+              mutate(species=gsub(pattern="_$", replacement = "", x = species)) %>% 
+              mutate(species=ifelse(is.na(word(species, 1, 2)), species, word(species, 1, 2))) %>% 
+              ungroup() %>% 
+              dplyr::select(-species0),
+            by="species") %>% 
+  dplyr::select(-species) %>% 
+  dplyr::select(`species0`, everything())
+
+##check for species without trait info
+traits.proz %>% 
+  filter_at(.vars=vars(-"species0"), 
+            all_vars(is.na(.))) %>% 
+  dim() ## [1] 16 53 # species with no trait info
+write_delim(traits.cov, path="_data/Mesobromion/traits.out.10perc.cov.txt", delim="\t")
 
 #### CORRELATION BETWEEN FUZZY WEIGHTED AND BEALS MATRICES
 #### WAS RUN IN THE CLUSTER WITH THE SCRIPT 01b_MesobromionCluster.R