From 1956678654ac4869479577ec7caec111b4063216 Mon Sep 17 00:00:00 2001
From: oa00oxef <gabriella.damasceno@idiv.de>
Date: Thu, 4 Apr 2024 14:00:03 +0000
Subject: [PATCH] Delete files from sPlot 2.0
---
code/splot_trait_match1.R | 543 --------------------------------------
1 file changed, 543 deletions(-)
delete mode 100644 code/splot_trait_match1.R
diff --git a/code/splot_trait_match1.R b/code/splot_trait_match1.R
deleted file mode 100644
index 4911976..0000000
--- a/code/splot_trait_match1.R
+++ /dev/null
@@ -1,543 +0,0 @@
-### Create the workspace for sPlot2.0 ###
-### written by Helge Bruelheide, 20.10.2016 ###
-
-# There are x files:
-# 1. DT.Rdata: a file in long format, including, among others,
-# PlotObservationID, species, Cover
-# and Relative.cover, while DT holds all original information,
-# including the "Matched concept" which is the unified name from
-# Turboveg 3,
-# DT2 has been shortened to the relevant fields and
-# contains only data with species that have a resolved name in the backbone
-# Both DT and DT contain species for which we do not have traits
-# 2. splot.header, read in from sPlot_2015_07_29_header.csv:
-# header file for DT, holding plot information on database origin,
-# country, lat, long, layer height and cover etc.
-# splot.header matches to DT, but not to DT2
-# 3. backbone.splot2.1.try3.Rdata: taxonomic backbone holding all sPlot and
-# TRY species names. These names are in names.sPlot.TRY, the resolved
-# names are in name.short.correct
-# 4. environment
-# 5. Export_sPlot_2016_09_12.csv: gap filled data from TRY3.0
-# 6. CWM.Rdata: Community weighted means based on all TRY gap filled data
-# 7. FD.Rao.Rdata, Rao's Q for all plots, using divc from ade4
-# 8. mpd.abu.Rdata, mntd.abu.Rdata, mpd.pa.Rdata, mntd.pa.Rdata
-# mean pairwise distance and mean nearest neighbour distance,
-# both based on abundances and presence/absence, using Picante
-
-library(data.table)
-
-load("/data/sPlot2.0/DT_20161021.RData") # server directory
-load("data/DT_20161021.RData") # local directory
-# 23586216 obs. of 14 variables.
-# the field "species" holdes the resolved name according to the backbone
-# the field "Matched concept" is the original Turboveg3 name.
-setkey(DT,PlotObservationID)
-
-splot.header <- fread("/data/sPlot2.0/sPlot_2015_07_29_header.csv",
- sep = "\t", na.strings=c("","NA"))
-# You get a warning, which you can ignore.
-# 1121244 rows and 43 columns.
-
-
-###### Taxonomic Backbone #####
-load("/data/sPlot2.0/backbone.splot2.1.try3.Rdata")
-str(backbone.splot2.1.try3)
-#data.frame': 130602 obs. of 33 variables:
-' $ Name_number : int 1 2 3 4 5 6 7 8 9 10 ...
-$ names.sPlot.TRY : chr "?" "0" "[1269 Chlorophytum platt]" "[1284 Echinochloa]" ...
-$ names.corr.string : chr "?" "0" "[1269 Chlorophytum platt]" "[1284 Echinochloa]" ...
-$ sPlot.TRY : chr "S" "S" "S" "S" ...
-$ Name_submitted : chr "Spermatophyta sp." "Spermatophyta sp." "Chlorophytum sp. [1269]" "Echinochloa sp." ...
-$ Overall_score : num 0 0 0.9 0.9 0.9 0.9 0.9 0 0.9 0 ...
-$ Name_matched : chr "No suitable matches found." "No suitable matches found." "Chlorophytum" "Echinochloa" ...
-$ Name_matched_rank : chr "" "" "genus" "genus" ...
-$ Name_score : num 0 0 1 1 1 1 1 0 1 0 ...
-$ Family_score : num 0 0 NA NA NA NA 1 0 1 0 ...
-$ Name_matched_accepted_family: chr "" "" "Asparagaceae" "Poaceae" ...
-$ Genus_matched : chr "" "" "Chlorophytum" "Echinochloa" ...
-$ Genus_score : num 0 0 1 1 1 1 NA 0 NA 0 ...
-$ Specific_epithet_matched : chr "" "" "" "" ...
-$ Specific_epithet_score : num 0 0 NA NA NA NA NA 0 NA 0 ...
-$ Unmatched_terms : chr "" "" "\"\"sp. [1269]" "\"\"sp." ...
-$ Taxonomic_status : chr "" "" "Accepted" "Accepted" ...
-$ Accepted_name : chr "" "" "Chlorophytum" "Echinochloa" ...
-$ Accepted_name_author : chr "" "" "" "" ...
-$ Accepted_name_rank : chr "" "" "genus" "genus" ...
-$ Accepted_name_url : chr "" "" "http://www.theplantlist.org/tpl1.1/search?q=Chlorophytum" "http://www.theplantlist.org/tpl1.1/search?q=Echinochloa" ...
-$ Accepted_name_species : chr "" "" "" "" ...
-$ Accepted_name_family : chr "" "" "Asparagaceae" "Poaceae" ...
-$ Selected : chr "true" "true" "true" "true" ...
-$ Source : chr "" "" "tpl" "tpl" ...
-$ Warnings : chr " " " " " " " " ...
-$ Manual.matching : chr NA NA NA NA ...
-$ Status.correct : chr "No suitable matches found." "No suitable matches found." "Accepted" "Accepted" ...
-$ name.correct : chr "No suitable matches found." "No suitable matches found." "Chlorophytum" "Echinochloa" ...
-$ rank.correct : chr "higher" "higher" "genus" "genus" ...
-$ family.correct : chr "" "" "Asparagaceae" "Poaceae" ...
-$ name.short.correct : chr NA NA "Chlorophytum" "Echinochloa" ...
-$ rank.short.correct : chr "higher" "higher" "genus" "genus" ...
-'
-# TRY 3.0
-TRY3.0 <- read.csv("/data/sPlot2.0/Export_sPlot_2016_09_12.csv")
-str(TRY3.0)
-#632938 obs. of 33 variables:
-any(is.na(TRY3.0[,c(2:19)])) #F
-any(TRY3.0[,c(2:19)]==0) #F
-
-
-index2 <- match(TRY3.0$Species,backbone.splot2.1.try3$names.sPlot.TRY)
-str(index2)
-TRY3.0$name.short.correct <- backbone.splot2.1.try3$name.short.correct[index2]
-library(stringr)
-TRY3.0$genus.short.correct <- word(TRY3.0$name.short.correct,1)
-head(TRY3.0[,c(34,35)],1000)
-TRY3.0$rank.short.correct <- backbone.splot2.1.try3$rank.short.correct[index2]
-table(TRY3.0$rank.short.correct, exclude=NULL)
-'family genus higher species <NA>
- 39 21021 1 611655 222 '
-
-### Calculations ###
-### take the log of all trait values in TRY3.0 ###
-for (i in 2:19){
- TRY3.0[,i] <- log(TRY3.0[,i])
-}
-any(is.na(TRY3.0[,c(2:19)])) #F
-
-TRY.all.n.3 <- aggregate(TRY3.0[,1], by=list(TRY3.0$name.short.correct), FUN=length)
-head(TRY.all.n.3)
-str(TRY.all.n.3) #52032 obs. of 2 variables:
-names(TRY.all.n.3)
-names(TRY.all.n.3) <- c("StandSpeciesName","n")
-
-TRY.all.mean.3 <- aggregate(TRY3.0[,c(2:19)], by=list(TRY3.0$name.short.correct), FUN=mean)
-str(TRY.all.mean.3) #52032 obs. of 19 variables:
-names(TRY.all.mean.3)
-' [1] "Group.1" "X1" "X4" "X11" "X13" "X14" "X15" "X18" "X26" "X27" "X47"
-[12] "X50" "X56" "X78" "X138" "X163" "X169" "X237" "X282" '
-names(TRY.all.mean.3) <- c("StandSpeciesName","LeafArea.mean", "StemDens.mean", "SLA.mean", "LeafC.perdrymass.mean",
- "LeafN.mean","LeafP.mean", "PlantHeight.mean", "SeedMass.mean", "Seed.length.mean",
- "LDMC.mean","LeafNperArea.mean", "LeafNPratio.mean", "Leaf.delta.15N.mean",
- "Seed.num.rep.unit.mean", "Leaffreshmass.mean", "Stem.cond.dens.mean",
- "Disp.unit.leng.mean", "Wood.vessel.length.mean")
-any(is.na(TRY.all.mean.3$SLA.mean)) #F
-
-TRY.all.sd.3 <- aggregate(TRY3.0[,c(2:19)], by=list(TRY3.0$name.short.correct), FUN=sd)
-str(TRY.all.sd.3) #52032 obs. of 19 variables:
-names(TRY.all.sd.3)
-names(TRY.all.sd.3) <- c("StandSpeciesName","LeafArea.sd", "StemDens.sd", "SLA.sd", "LeafC.perdrymass.sd",
- "LeafN.sd","LeafP.sd", "PlantHeight.sd", "SeedMass.sd", "Seed.length.sd",
- "LDMC.sd","LeafNperArea.sd", "LeafNPratio.sd", "Leaf.delta.15N.sd",
- "Seed.num.rep.unit.sd", "Leaffreshmass.sd", "Stem.cond.dens.sd",
- "Disp.unit.leng.sd", "Wood.vessel.length.sd")
-any(is.na(TRY.all.sd.3$SLA.sd)) #T
-TRY.all.mean.sd.3.by.taxon <- data.frame(TRY.all.n.3,TRY.all.mean.3[,c(2:19)],TRY.all.sd.3[,c(2:19)])
-str(TRY.all.mean.sd.3.by.taxon) #52032 obs. of 38 variables
-'data.frame: 52032 obs. of 38 variables:
-$ StandSpeciesName : chr "Aa" "Aaronsohnia pubescens" "Abarema" "Abarema adenophora" ...
- $ n : int 1 1 8 4 1 1 1 38 4 87 ...
-$ LeafArea.mean : num 6.61 5.33 7.2 6.96 7.37 ...
-$ StemDens.mean : num -0.822 -0.823 -0.534 -0.427 -1.02 ...
-$ SLA.mean : num 2.24 3.09 2.43 2.38 2.7 ...
-$ LeafC.perdrymass.mean : num 6.17 6.11 6.18 6.26 6.14 ...
-$ LeafN.mean : num 2.92 3.07 3.3 3.19 3.38 ...
-$ LeafP.mean : num -0.0143 1.0433 -0.1211 0.3403 0.4916 ...
-$ PlantHeight.mean : num -0.498 -1.611 2.598 2.874 1.83 ...
-$ SeedMass.mean : num -4.08 -1.69 4.27 4.49 4.26 ...
-$ Seed.length.mean : num -0.317 0.337 1.949 2.021 2.244 ...
-$ LDMC.mean : num -1.471 -1.586 -1.01 -0.798 -0.986 ...
-$ LeafNperArea.mean : num 0.8956 0.0878 0.8763 0.8343 0.6596 ...
-$ LeafNPratio.mean : num 2.54 1.95 3.49 3.26 3.06 ...
-$ Leaf.delta.15N.mean : num 0.38 0.4 0.888 1.258 1.36 ...
-$ Seed.num.rep.unit.mean : num 9.56 10.73 2.04 1.64 5.76 ...
-$ Leaffreshmass.mean : num -1.3303 -2.7662 0.0832 -0.2516 -0.7861 ...
-$ Stem.cond.dens.mean : num 3.67 4.47 1.77 2.14 2.78 ...
-$ Disp.unit.leng.mean : num -0.555 0.445 2.734 2.916 2.235 ...
-$ Wood.vessel.length.mean: num 6.13 5.32 5.88 5.73 5.32 ...
-$ LeafArea.sd : num NA NA 0.0698 0.0303 NA ...
-$ StemDens.sd : num NA NA 0.00778 0.11012 NA ...
-$ SLA.sd : num NA NA 0.0227 0.1063 NA ...
-$ LeafC.perdrymass.sd : num NA NA 0.0014 0.0086 NA ...
-$ LeafN.sd : num NA NA 0.025 0.0638 NA ...
-$ LeafP.sd : num NA NA 0.0614 0.3936 NA ...
-$ PlantHeight.sd : num NA NA 0.053 0.0626 NA ...
-$ SeedMass.sd : num NA NA 0.0684 0.0315 NA ...
-$ Seed.length.sd : num NA NA 0.0307 0.0457 NA ...
-$ LDMC.sd : num NA NA 0.0346 0.0553 NA ...
-$ LeafNperArea.sd : num NA NA 0.0412 0.098 NA ...
-$ LeafNPratio.sd : num NA NA 0.0354 0.291 NA ...
-$ Leaf.delta.15N.sd : num NA NA 0.0326 0.1651 NA ...
-$ Seed.num.rep.unit.sd : num NA NA 0.192 0.211 NA ...
-$ Leaffreshmass.sd : num NA NA 0.0702 0.0748 NA ...
-$ Stem.cond.dens.sd : num NA NA 0.413 0.139 NA ...
-$ Disp.unit.leng.sd : num NA NA 0.033 0.0927 NA ...
-$ Wood.vessel.length.sd : num NA NA 0.288 0.147 NA ...'
-
-save(TRY.all.mean.sd.3.by.taxon, file="/data/sPlot2.0/TRY.all.mean.sd.3.by.taxon.Rdata")
-
-
-TRY.all.n.3 <- aggregate(TRY3.0[,1], by=list(TRY3.0$genus.short.correct), FUN=length)
-head(TRY.all.n.3)
-str(TRY.all.n.3) #7873 obs. of 2 variables:
-names(TRY.all.n.3)
-names(TRY.all.n.3) <- c("StandSpeciesName","n")
-
-TRY.all.mean.3 <- aggregate(TRY3.0[,c(2:19)], by=list(TRY3.0$genus.short.correct), FUN=mean)
-str(TRY.all.mean.3) #7873 obs. of 19 variables:
-names(TRY.all.mean.3)
-' [1] "Group.1" "X1" "X4" "X11" "X13" "X14" "X15" "X18" "X26" "X27" "X47"
-[12] "X50" "X56" "X78" "X138" "X163" "X169" "X237" "X282" '
-names(TRY.all.mean.3) <- c("StandSpeciesName","LeafArea.mean", "StemDens.mean", "SLA.mean", "LeafC.perdrymass.mean",
- "LeafN.mean","LeafP.mean", "PlantHeight.mean", "SeedMass.mean", "Seed.length.mean",
- "LDMC.mean","LeafNperArea.mean", "LeafNPratio.mean", "Leaf.delta.15N.mean",
- "Seed.num.rep.unit.mean", "Leaffreshmass.mean", "Stem.cond.dens.mean",
- "Disp.unit.leng.mean", "Wood.vessel.length.mean")
-any(is.na(TRY.all.mean.3$SLA.mean)) #F
-
-TRY.all.sd.3 <- aggregate(TRY3.0[,c(2:19)], by=list(TRY3.0$genus.short.correct), FUN=sd)
-str(TRY.all.sd.3) #7873 obs. of 19 variables:
-names(TRY.all.sd.3)
-names(TRY.all.sd.3) <- c("StandSpeciesName","LeafArea.sd", "StemDens.sd", "SLA.sd", "LeafC.perdrymass.sd",
- "LeafN.sd","LeafP.sd", "PlantHeight.sd", "SeedMass.sd", "Seed.length.sd",
- "LDMC.sd","LeafNperArea.sd", "LeafNPratio.sd", "Leaf.delta.15N.sd",
- "Seed.num.rep.unit.sd", "Leaffreshmass.sd", "Stem.cond.dens.sd",
- "Disp.unit.leng.sd", "Wood.vessel.length.sd")
-any(is.na(TRY.all.sd.3$SLA.sd)) #T
-
-TRY.all.mean.sd.3.by.genus.species <- TRY.all.mean.sd.3.by.taxon
-names(TRY.all.mean.sd.3.by.genus.species)
-index3 <- match(TRY.all.mean.sd.3.by.taxon$StandSpeciesName,
- backbone.splot2.1.try3$name.short.correct)
-any(is.na(index3)) #F
-
-which(backbone.splot2.1.try3$rank.short.correct[index3]=="genus")
-table(backbone.splot2.1.try3$rank.short.correct[index3])
-'family genus higher species
- 6 2342 1 49683 '
-#backbone.splot2.1.try3$rank.short.correct[index3]=="genus"
-
-index4 <- match(TRY.all.mean.sd.3.by.genus.species[backbone.splot2.1.try3$rank.short.correct[index3]=="genus",1],
- TRY.all.n.3$StandSpeciesName)
-str(index4)
-
-
-TRY.all.mean.sd.3.by.genus.species[backbone.splot2.1.try3$rank.short.correct[index3]=="genus",2] <- TRY.all.n.3[index4,2]
-for (i in 1:18){
- TRY.all.mean.sd.3.by.genus.species[backbone.splot2.1.try3$rank.short.correct[index3]=="genus",i+2] <- TRY.all.mean.3[index4,i+1]
- TRY.all.mean.sd.3.by.genus.species[backbone.splot2.1.try3$rank.short.correct[index3]=="genus",i+20] <- TRY.all.sd.3[index4,i+1]
-}
-head(TRY.all.mean.sd.3.by.genus.species[,c(1:3)],50)
-head(TRY.all.mean.sd.3.by.taxon[,c(1:3)],50)
-save(TRY.all.mean.sd.3.by.genus.species, file="/data/sPlot2.0/TRY.all.mean.sd.3.by.genus.species.Rdata")
-#load("/data/sPlot2.0/TRY.all.mean.sd.3.by.genus.species.Rdata")
-any(is.na(TRY.all.mean.sd.3.by.genus.species[,c(3:21)]))
-#T
-any(is.na(TRY.all.mean.sd.3.by.taxon[,c(3:21)]))
-#T
-###
-
-
-any(is.na(DT$species)) #T
-# there NA cases as not all "Matched concept" names in DT have resolved names
-length(unique(DT$PlotObservationID)) #1121244
-length(DT$species[!is.na(DT$species)]) #23555942
-length(DT$species) #23586216
-23586216-23555942 # 30274 NA names!!!
-# it gives nonsense to match them with traits
-
-index7 <- match(DT$species,TRY.all.mean.sd.3.by.genus.species$StandSpeciesName)
-length(index7) #23586216
-length(index7[!is.na(index7)])
-# 21172989, with TRY3.0
-# 21040927, with TRY2.0
-# 19841429, with first TRY version
-23586216 - 21172989 # 2413227 entries have no species in CWM
-# traits are fewer as not all CWM rows have traits
-(23586216 - 21172989)/23586216*100
-# 10.23151% of all entries
-(21172989)/23586216*100
-# which are 89.76849% of all entries
-# previously: 86.79555% with TRY2.0
-# previously: 18.15247 % and with first TRY version
-
-DT2 <- DT[!is.na(DT$species),]
-any(is.na(DT2$Relative.cover)) # F
-length(unique(DT2$PlotObservationID)) #1121145
-#length(unique(sPlot3$PlotObservationID)) #1121245
-names(DT2)
-names(DT2)[3] <- "Taxon.group"
-names(DT2)[6] <- "Matched.concept"
-names(DT2)
-library(dplyr)
-DT2 <- select(DT2,PlotObservationID,species, Taxon.group, Matched.concept, Layer, Cover, Relative.cover)
-str(DT2) #23555942 obs. of 7 variables:
-length(unique(DT2$PlotObservationID)) #1121145
-plot.total.cover4 <- DT2[, list(total.cover=sum(Cover)), by=PlotObservationID]
-str(plot.total.cover4) #1121145
-min(plot.total.cover4$total.cover) #0.001
-max(plot.total.cover4$total.cover) #104704.1
-any(is.na(plot.total.cover4$total.cover)) # F
-length(plot.total.cover4$total.cover[plot.total.cover4$total.cover==0])
-# 0 !!!
-# all plots have cover
-
-index4 <- match(DT2$PlotObservationID,plot.total.cover4$PlotObservationID)
-length(index4) #23555942
-any(is.na(index4)) # F
-
-# recalculate Relative cover
-### CAREFUL! This has to be done by layer now ###
-DT2$Relative.cover <- DT2$Cover/plot.total.cover4$total.cover[index4]
-
-plot.total.cover5 <- DT2[, list(total.cover=sum(Relative.cover)), by=PlotObservationID]
-min(plot.total.cover5$total.cover) #1
-max(plot.total.cover5$total.cover) #1
-
-save(DT2,file = "/data/sPlot2.0/DT2_20161021.RData")
-
-
-length(unique(DT2$PlotObservationID)) #1121145 versus 1121244 before
-index7 <- match(DT2$species,TRY.all.mean.sd.3.by.genus.species$StandSpeciesName)
-length(index7) #23555942
-length(index7[!is.na(index7)])
-# 21172989, with TRY3.0
-23555942 - 21172989
-# 2382953 entries with valid species names have no species in the
-# gap-filled trait file
-# not all species there have traits
-(23555942 - 21172989)/23555942*100
-# 10.11614% of all entries have no traits
-(21172989)/23555942*100
-# which are 89.88386% of all entries
-
-### CWM ###
-mean(TRY.all.mean.sd.3.by.genus.species$SLA.mean) # NA
-mean(TRY.all.mean.sd.3.by.genus.species$SLA.mean,na.rm=T) # 2.63365
-# example
-DT2$trait <- NA
-DT2$trait <- TRY.all.mean.sd.3.by.genus.species$SLA.mean[index7]
-length(DT2$trait[!is.na(DT2$trait)]) # 21172949
-length(DT2$trait[is.na(DT2$trait)]) # 2382993
-(21172949-2382993)/21172949
-# 0.8874511 % of all records in sPlot have a valid trait
-length(unique(DT2$species)) #61131
-length(unique(DT2$species[!is.na(DT2$trait)])) #26666
-26666/61131 # 0.4362108
-str(DT2)
-
-colnames(TRY.all.mean.sd.3.by.genus.species)
-CWM2 <- DT2[,list(CWM.SLA = weighted.mean(trait,Cover,na.rm = T)),by=PlotObservationID]
-# I have checked that we do not need to prefilter only those entries that have a trait value
-# this works fine
-str(CWM2)
-dim(CWM2) #1121145
-which(colnames(TRY.all.mean.sd.3.by.genus.species)=="LeafArea.mean") # 3
-which(colnames(TRY.all.mean.sd.3.by.genus.species)=="Wood.vessel.length.mean") # 20
-CWM <- array(NA,c(dim(CWM2)[1],18),dimnames=list(CWM2$PlotObservationID,
- colnames(TRY.all.mean.sd.3.by.genus.species)[3:20]))
-rm(CWM2)
-for (i in 1:18){
- DT2$trait <- NA
- DT2$trait <- TRY.all.mean.sd.3.by.genus.species[index7,i+2]
- CWM[,i] <- DT2[,list(CWM.trait= weighted.mean(trait,Relative.cover,na.rm = T)),by=PlotObservationID]$CWM.trait
-}
-dim(CWM) #1121145 18
-length(CWM[,"SLA.mean"][!is.na(CWM[,"SLA.mean"])]) #1116113
-1116113/1121145 # 0.9955117% of all plots in sPlot2 have a CWM value
-
-save(CWM, file="/data/sPlot2.0/CWM.Rdata") # on the server
-save(CWM, file="data/CWM.Rdata") # on the local system
-#load("/data/sPlot2.0/CWM.Rdata") # on the server
-
-## FD Raos Q based on divc in ade4
-library(ade4) # f?r divc (Rao's Q)
-FD.fun <- function(trait, abu){
- res <- as.double(NA)
- if (length(trait[!is.na(trait)])>0){
- res <- 0
- #nam <- as.character(nam[!is.na(trait)])
- abu <- as.data.frame(abu[!is.na(trait)])
- #rownames(abu) <- nam
- trait <- as.data.frame(trait[!is.na(trait)])
- #rownames(trait) <- nam
- dis <- dist(trait,method="euclidean")
- if (sum(dis)>1){
- sqrt.dis <- sqrt(dis)
- # taking the square root is required to obtain the same result from the divc function
- # as divc takes the square of distances values, as suggested by
- # Champely & Chessel (2002, Env. Ecol. Stat. 9: 167-177)
- #abu <- as.data.frame(abu)
- res <- unlist(divc(abu, sqrt.dis))
- #Rao's Q: sumi sumj (dist ij prop i prop j)'
- }
- }
- res
-}
-
-
-library(picante)
-# define function for mean pairwise distance
-'mpd.fun.dt <- function(abu, dis, nam, abundance.weighted){
- abu <- t(abu)
- colnames(abu) <- nam
- res <- mpd(abu, dis, abundance.weighted=abundance.weighted)
-}
-# define funtion of mean nearest neighbor distance
-mntd.fun.dt <- function(abu, dis, nam, abundance.weighted){
- abu <- t(abu)
- colnames(abu) <- nam
- res <- mntd(abu, dis, abundance.weighted=abundance.weighted)
- res
-}
-'
-
-mpd.fun <- function(nam, trait, abu,abundance.weighted){
- #res <- numeric(1) # carries result from the function
- res <- as.double(NA)
- nam <- nam[!is.na(trait)]
- abu <- abu[!is.na(trait)]
- trait <- trait[!is.na(trait)]
- if (length(trait)>0){
- # dis <- as.matrix(dist(t(t1),method="euclidean"))
- abu <- t(abu)
- colnames(abu) <- nam
- trait <- t(trait)
- colnames(trait) <- nam
- #dis <- as.matrix(vegdist(t(t1),method="euclidean", na.rm=T))
- dis <- as.matrix(dist(t(trait),method="euclidean"))
- res <- mpd(abu, dis, abundance.weighted=abundance.weighted)
- }
- res
-}
-mntd.fun <- function(nam, trait, abu,abundance.weighted){
- #res <- numeric(1) # carries result from the function
- res <- as.double(NA)
- nam <- nam[!is.na(trait)]
- abu <- abu[!is.na(trait)]
- trait <- trait[!is.na(trait)]
- if (length(trait)>0){
- # dis <- as.matrix(dist(t(t1),method="euclidean"))
- abu <- t(abu)
- colnames(abu) <- nam
- trait <- t(trait)
- colnames(trait) <- nam
- #dis <- as.matrix(vegdist(t(t1),method="euclidean", na.rm=T))
- dis <- as.matrix(dist(t(trait),method="euclidean"))
- res <- mntd(abu, dis, abundance.weighted=abundance.weighted)
- }
- res
-}
-test<- DT[c(1:10000),list(mpd.trait= mpd.fun(species, trait, Relative.cover, abundance.weighted=TRUE)),by=PlotObservationID]
-str(test)
-length(test$mpd.trait[is.na(test$mpd.trait)])
-head(test$mpd.trait)
-DT[PlotObservationID==30,]
-test$mpd.trait[test$PlotObservationID==30]
-length(test$mntd.trait[is.na(test$mntd.trait)])
-
-test<- DT[c(1:10000),list(mntd.trait= mntd.fun(species, trait, Relative.cover, abundance.weighted=TRUE)),by=PlotObservationID]
-test$mntd.trait[test$PlotObservationID==30]
-
-FD.Rao <- array(NA,c(dim(CWM)[1],18),dimnames=list(dimnames(CWM)[[1]],
- colnames(TRY.all.mean.sd.3.by.genus.species)[3:20]))
-mpd.abu <- array(NA,c(dim(CWM)[1],18),dimnames=list(dimnames(CWM)[[1]],
- colnames(TRY.all.mean.sd.3.by.genus.species)[3:20]))
-mpd.pa <- array(NA,c(dim(CWM)[1],18),dimnames=list(dimnames(CWM)[[1]],
- colnames(TRY.all.mean.sd.3.by.genus.species)[3:20]))
-mntd.abu <- array(NA,c(dim(CWM)[1],18),dimnames=list(dimnames(CWM)[[1]],
- colnames(TRY.all.mean.sd.3.by.genus.species)[3:20]))
-mntd.pa <- array(NA,c(dim(CWM)[1],18),dimnames=list(dimnames(CWM)[[1]],
- colnames(TRY.all.mean.sd.3.by.genus.species)[3:20]))
-
-str(FD.Rao) #logi [1:1121145, 1:18]
-
-for (i in 1:18){
- print(i)
- DT2$trait <- NA
- DT2$trait <- TRY.all.mean.sd.3.by.genus.species[index7,i+2]
- #FD.Rao[,i] <- DT2[,list(FD.trait= FD.fun(trait, Relative.cover)),by=PlotObservationID]$FD.trait
- #mpd.abu[,i] <- DT2[,list(mpd.trait= mpd.fun(species, trait, Relative.cover, abundance.weighted=TRUE)),by=PlotObservationID]$mpd.trait
- mpd.pa[,i] <- DT2[,list(mpd.trait= mpd.fun(species, trait, Relative.cover, abundance.weighted=FALSE)),by=PlotObservationID]$mpd.trait
- mntd.abu[,i] <- DT2[,list(mntd.trait= mntd.fun(species, trait, Relative.cover, abundance.weighted=TRUE)),by=PlotObservationID]$mntd.trait
- #mntd.pa[,i] <- DT2[,list(mntd.trait= mntd.fun(species, trait, Relative.cover, abundance.weighted=FALSE)),by=PlotObservationID]$mntd.trait
-}
-save(FD.Rao, file="/data/sPlot2.0/FD.Rao.Rdata") # on the server
-save(mpd.abu, file="/data/sPlot2.0/mpd.abu.Rdata") # on the server
-save(mpd.pa, file="/data/sPlot2.0/mpd.pa.Rdata") # on the server
-save(mntd.abu, file="/data/sPlot2.0/FD.Rao.Rdata") # on the server
-save(mntd.pa, file="/data/sPlot2.0/mntd.pa.Rdata") # on the server
-dim(mpd.pa) #1121145 18
-length(mpd.pa[,"SLA.mean"][!is.na(mpd.pa[,"SLA.mean"])]) #1100058
-1100058/1121145 #0.9811915
-
-library(vegan)
-head(dimnames(TRY.all.mean.sd.3.by.genus.species)[[1]])
-dimnames(TRY.all.mean.sd.3.by.genus.species)[[2]]
-TRY.all.mean.sd.3.by.genus.species2 <- TRY.all.mean.sd.3.by.genus.species[!is.na(TRY.all.mean.sd.3.by.genus.species[,"SLA.mean"]),]
-trait.pca1 <- rda(TRY.all.mean.sd.3.by.genus.species2[,c(5,9,10)],scale=T)
-## do this for 3 traits
-nam <- DT2$species[DT$PlotObservationID==1]
-pca1 <- DT2$pca1[DT$PlotObservationID==1]
-pca2 <- DT2$pca2[DT$PlotObservationID==1]
-pca3 <- DT2$pca3[DT$PlotObservationID==1]
-abu <- DT2$Relative.cover[DT$PlotObservationID==1]
-
-mpd.fun.multitrait <- function(nam, pca1, pca2, pca3, abu,abundance.weighted){
- #res <- numeric(1) # carries result from the function
- trait <- as.matrix(cbind(pca1, pca2, pca3))
- res <- as.double(NA)
- nam <- nam[!is.na(rowSums(trait))]
- abu <- abu[!is.na(rowSums(trait))]
- trait <- trait[!is.na(rowSums(trait)),]
- if (length(trait)>3){
- # then there is more than one species (with 3 trait values)
- abu <- t(abu)
- colnames(abu) <- nam
- trait <- t(trait)
- colnames(trait) <- nam
- dis <- as.matrix(dist(t(trait),method="euclidean"))
- res <- mpd(abu, dis, abundance.weighted=abundance.weighted)
- }
- res
-}
-mntd.fun.multitrait <- function(nam, pca1, pca2, pca3, abu,abundance.weighted){
- #res <- numeric(1) # carries result from the function
- trait <- as.matrix(cbind(pca1, pca2, pca3))
- res <- as.double(NA)
- nam <- nam[!is.na(rowSums(trait))]
- abu <- abu[!is.na(rowSums(trait))]
- trait <- trait[!is.na(rowSums(trait)),]
- if (length(trait)>3){
- abu <- t(abu)
- colnames(abu) <- nam
- trait <- t(trait)
- colnames(trait) <- nam
- dis <- as.matrix(dist(t(trait),method="euclidean"))
- res <- mntd(abu, dis, abundance.weighted=abundance.weighted)
- }
- res
-}
-
-identical(dimnames(TRY.all.mean.sd.3.by.genus.species2)[[1]],
- dimnames(summary(trait.pca1)$sites)[[1]])
-#T
-index8 <- match(DT2$species,TRY.all.mean.sd.3.by.genus.species2$StandSpeciesName)
-length(index8) #23555942
-length(index8[!is.na(index8)]) #21172949
-
-
-DT2$pca1 <- summary(trait.pca1)$sites[index8,1]
-DT2$pca2 <- summary(trait.pca1)$sites[index8,2]
-DT2$pca3 <- summary(trait.pca1)$sites[index8,3]
-names(DT2)
-# pca scores in 9:11
-mpd.abu.LHS <- DT2[,list(mpd.trait= mpd.fun.multitrait(species, pca1, pca2, pca3, Relative.cover, abundance.weighted=TRUE)),by=PlotObservationID]$mpd.trait
-mpd.pa.LHS <- DT2[,list(mpd.trait= mpd.fun.multitrait(species, pca1, pca2, pca3, Relative.cover, abundance.weighted=FALSE)),by=PlotObservationID]$mpd.trait
-mntd.abu.LHS <- DT2[,list(mntd.trait= mntd.fun.multitrait(species, pca1, pca2, pca3, Relative.cover, abundance.weighted=TRUE)),by=PlotObservationID]$mntd.trait
-mntd.pa.LHS <- DT2[,list(mntd.trait= mntd.fun.multitrait(species, pca1, pca2, pca3, Relative.cover, abundance.weighted=FALSE)),by=PlotObservationID]$mntd.trait
-
-mpd.mntd.LHS <- data.frame(mpd.abu.LHS,mpd.pa.LHS,mntd.abu.LHS, mntd.pa.LHS)
-str(mpd.mntd.LHS)
-#data.frame': 1121145 obs. of 4 variables:
-save(mpd.mntd.LHS, file="/data/sPlot2.0/mpd.mntd.LHS.Rdata") # on the server
-
--
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