From e723adc4940a66f93d3e5aefc097b63b4ae10466 Mon Sep 17 00:00:00 2001
From: Francesco Sabatini <francesco.sabatini@idiv.de>
Date: Tue, 4 Aug 2020 15:51:06 +0200
Subject: [PATCH] Prepared ALSO data based on species covers
---
00_Mesobromion_DataPreparation.R | 191 +++++++++++++++++++------------
1 file changed, 120 insertions(+), 71 deletions(-)
diff --git a/00_Mesobromion_DataPreparation.R b/00_Mesobromion_DataPreparation.R
index a12a4be..e825207 100644
--- a/00_Mesobromion_DataPreparation.R
+++ b/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
--
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