Diversity index: Difference between revisions

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* abundancies of species, i.e. proportions of individuals belonging to each species among the whole population (one entry per species).
* abundancies of species, i.e. proportions of individuals belonging to each species among the whole population (one entry per species).


==Rationale==
=== Calculations ===
 
{{hidden|
<pre>
# Sp accumulation ichno simple
# version 2014-11-09 by Hanna Tuomisto
 
# PART 1
# open a data tables with data of species per trap sample, environmental data and literature data
# combine samples from the same trap, by soil type and by region
# combine data within traps by sampling season
# export the resulting data tables to text files
 
# PART 2
# open data saved in PART 1
# calculate number of species and its chao and ACE estimates, number of individuals, diversity
# save resulting table to a new file
 
# PART 3
# open data saved in PART 2 and construct and save species-accumulation graphs
 
# PART 4
# run NMDS with trapwise data and save the graphs
 
 
 
########## PART 1: Open data files and combine data ##########
 
#### check default directory
getwd()
setwd("/Users/hantuo/Documents/j1 Anun koinobiontit") # for iMac
setwd("/Users/hantuo/Documents/j1 Isrraelin iknot") # for iMac
setwd("/Users/hannatuomisto/Documents/j1 Isrraelin iknot") # for Air
setwd("/Users/hannatuomisto/Documents/Dokumentit/j1 Anun koinobiontit") # for Pro
setwd("/Users/hannatuomisto/Documents/Dokumentit/j1 Isrraelin iknot") # for Pro
 
 
###### open raw data files ######
 
# open sites by species data file for the own field data
# columns should be as follows: "TrapID","SeqInTrap","SampleCode","StartYear","StartMonth","StartDay",
# "EndYear","EndMonth","EndDay", further columns each corresponding to one species
path <- file.choose()
spraw <- read.csv(path, header=TRUE, stringsAsFactors = FALSE)
spname <- gsub(".csv", "", basename(path), ignore.case=TRUE)
 
# open file with trap locality info
# columns should be as follows: "TrapID","SoilType","Region","Latitude"
path <- file.choose()
trapraw <- read.csv(path, header=TRUE, stringsAsFactors = FALSE)
trapname <- gsub(".csv", "", basename(path), ignore.case=TRUE)
 
# eliminate traps that are only found in one of the data files
sptable <- spraw[spraw$TrapID %in% trapraw$TrapID,]
traptable <- trapraw[trapraw$TrapID %in% spraw$TrapID,]
if((el=nrow(spraw) - nrow(sptable)) != 0) {
  print(paste(el, "species data rows will be omitted from analyses because trap info is not available for:",
        paste0(spraw[!spraw$TrapID %in% trapraw$TrapID,"TrapID"], collapse=", ")))
}
if((el=nrow(trapraw) - nrow(traptable)) != 0) {
  print(paste(el, "traps had no species data:", paste0(trapraw[!trapraw$TrapID %in% spraw$TrapID,"TrapID"], collapse=", ")))
}
 
# open file with literature data
# columns should contain: "MTM", "Species", "Individuals", "Latitude", "Longitude", Elev.min", "Elev.max"
# coordinates can also be: "Lat.deg", "Lat.min"
path <- file.choose()
litraw <- read.csv(path, header=TRUE)
litname <- gsub(".csv", "", basename(path), ignore.case=TRUE)
if(FALSE) { # if needed, convert latitudes to decimal degrees by running the next line:
  litraw[,ncol(litraw+1)] <- litraw$Lat.deg + litraw$Lat.min/60
}
 
 
###### choose options and prepare the data ######
 
#### choose what combinations of sampling periods to use
comb <- c("TrapID", "SoilType", "Region")
comb <- c("TrapID", "Region")
comb <- c("TrapID")
comb <- c("Region")
comb <- c("SoilType")
 
#### set name for file with species accumulation data
# info on combination will be added automatically
# !!! any file with the same name in the working directory will be overwritten without warning !!!
filename <- paste(spname,"sp-accum", sep="_")
 
 
#### Calculate catch for different sampling periods and traps
 
# convert start and end date to sequential day
days <- cbind(1:12, c(31,28,31,30,31,30,31,31,30,31,30,31), c(rep(0,12)))
colnames(days) <- c("month", "monthlength", "cumdays")
for(i in 2:nrow(days)) days[i,3] <- days[i-1,2]+days[i-1,3]
StartDay <- (sptable$StartYear-min(sptable$StartYear))*365+sptable$StartDay+days[sptable$StartMonth,3]
EndDay <- (sptable$EndYear-min(sptable$StartYear))*365+sptable$EndDay+days[sptable$EndMonth,3]
NumDays <- EndDay-StartDay
SamplingGap <- 0
 
# create a new data matrix with sequential days
data <- cbind(TrapID=sptable$TrapID, StartDay, EndDay, NumDays, SamplingGap, sptable[,10:ncol(sptable)])
data <- na.omit(data)  # eliminate rows with missing dates
# aggregate data by trap
data_tr <- aggregate(data[,-1], by=list(data$TrapID), FUN=sum, simplify=TRUE)
colnames(data_tr)[1] <- colnames(data)[1]
# add SoilType and Region (but not Latitude)
data_sub <- merge(traptable[,-4], data) 
data_tr <- merge(traptable[,-4], data_tr) 
# fix start and end days
data_tr$StartDay <- aggregate(data$StartDay, by=list(data$TrapID), FUN=min, simplify=TRUE)[,2]
data_tr$EndDay <- aggregate(data$EndDay, by=list(data$TrapID), FUN=max, simplify=TRUE)[,2]
data_tr$SamplingGap <- data_tr$EndDay - data_tr$StartDay - data_tr$NumDays
 
# run data aggregation within and across traps
for(m in seq(length(comb))) { # loop through the aggregation criteria
  if(comb[m]=="TrapID") {
    data_m <- data_sub
  } else {  # use data aggregated by trap
    data_m <- data_tr
    if(comb[m]=="SoilType") data_m <- data_m[data_m$SoilType != "",]  # exclude traps with no SoilType
  }
  # create matrix to compile data on sampling times and numbers of species
  sp_time <- data_m
  for(n in seq(unique(data_m$Region))) {  # repeat for each region separately
  comb_n <- data_m[data_m$Region==unique(data_m$Region)[n],] 
  for(p in if(comb[m]=="TrapID") 1 else 1:10) {
  for(i in seq(unique(comb_n[,comb[m]]))) {  # loop through the unique values of the m:th aggregation criterion
  comb_i <- comb_n[comb_n[,comb[m]]==unique(comb_n[,comb[m]])[i],]
 
  if(nrow(comb_i) > 1) {
      if(comb[m]=="TrapID") {
      comb_i <- comb_i[order(comb_i$StartDay, comb_i$EndDay),]  # sort rows by date
    } else {
      comb_i <- comb_i[sample(nrow(comb_i), nrow(comb_i)),]  # randomize row order
    }
      first <- comb_i[1,]
      for(k in 2:(nrow(comb_i))) {
        second <- comb_i[k,]
        new <- nrow(sp_time)+1
        sp_time[new,"Region"] <- first$Region  # this creates a new row
        sp_time[new,"SoilType"] <- if(first$SoilType==second$SoilType) {first$SoilType} else {paste(first$SoilType,second$SoilType,sep=" ")}
        sp_time[new,"TrapID"] <- if(first$TrapID==second$TrapID) {first$TrapID} else {paste(first$TrapID,second$TrapID,sep=" ")}
        sp_time[new,"StartDay"] <- min(first$StartDay,second$StartDay)
        sp_time[new,"EndDay"] <- max(first$EndDay,second$EndDay)
        sp_time[new,"NumDays"] <- sum(first$NumDays,second$NumDays)
       
          if(comb[m]=="TrapID") {
            sp_time[new,"SamplingGap"] <- max(first$StartDay,second$StartDay)-min(first$EndDay,second$EndDay)+first$SamplingGap+second$SamplingGap
          } else {sp_time[new,"SamplingGap"] <- NA}
       
          sp_time[new,8:ncol(sp_time)] <- first[,8:ncol(first)]+second[,8:ncol(second)]
          # redefine first to be the combination of first and second
          first <- sp_time[new,]
      }   
  }
}
}
}
write.csv(sp_time, file=paste(filename, "_", comb[m], ".csv", sep=""))
}
 
 
 
########## PART 2: Calculate number of individuals and species, estimates of S and diversity ##########
 
library(vegan)
 
#### open the file with aggregated sampling periods to be used in calculations
path <- file.choose()
to_est <- read.csv(path, row.names=1, stringsAsFactors = FALSE)
to_estname <- gsub(".csv", "", basename(path), ignore.case=TRUE)
 
 
#### calculate individuals, species, estimates and diversity and save as table
estS <- t(estimateR(to_est[,8:ncol(to_est)]))
estSadd <- cbind("ChaoAddRel"=100*(estS[,"S.chao1"]/estS[,"S.obs"]-1),
                "ChaoAddAbs"=estS[,"S.chao1"]-estS[,"S.obs"])
renyiD <- renyi(to_est[,8:ncol(to_est)], scales=c(0,1,2), hill=TRUE)
colnames(renyiD) <- paste("Div.q", colnames(renyiD), sep="")
estSdiv <- cbind("Region"=to_est$Region, "MTM"=to_est$NumDays/30, "StartDay"=to_est$StartDay,
                "Individuals"=rowSums(to_est[,8:ncol(to_est)]), estS[,-(4:5)],
                estSadd, renyiD, "TrapID"=to_est$TrapID, "SoilType"=to_est$SoilType)
colnames(estSdiv)[which(colnames(estSdiv)=="S.obs")] <- "Species"
write.csv(estSdiv, file=paste(to_estname, "_S_est_D.csv", sep=""))
 
 
 
########## PART 3: Make species accumulation graphs ##########
 
#### open the result file for plotting, name ends in "_S_est_D.csv" ####
path <- file.choose()
toplot <- read.csv(path, row.names=1, stringsAsFactors = FALSE)
toplotname <- gsub(".csv", "", basename(path), ignore.case=TRUE)
if(any("Species" %in% colnames(toplot))) {"OK"} else {"The opened file has no Species column!"}
 
#### choose if source file name is used as title for the plot ####
title <- toplotname
# title <- ""  # choose this for publication quality
 
#### choose the color scheme ####
col_choice <- 1  # 6 colors by soil type; optimised for Clay, Floodplain, Loam, Sand, Secondary, Terrace
# col_choice <- 5  # as 1, but add abbreviation of Region to legend
# col_choice <- 2  # 2 colours by Region
# col_choice <- 6  # all symbols black
# col_choice <- 3  # 4 colours by SoilType (Loreto) NOT FUNCTIONAL YET
# col_choice <- 4  # 1 colour (all sites the same) NOT FUNCTIONAL YET 
 
# the colors are automatically defined here on the basis of the above choice
if((col_choice==1) | (col_choice==5)) { 
  col_lut <- cbind("SoilType"=c(sort(unique(toplot$SoilType))), "Color"=c("darkgoldenrod","blue","brown4","red","darkgreen","darkblue")
                  , "Rank"=c(1,5,2,3,4,6))
  col_lut <- col_lut[order(col_lut[,"Rank"]),]
  col_source <- "SoilType"
  if(col_choice==5) {
    col_lut <- cbind(col_lut, "Reg.abbr"=col_lut[,"SoilType"])
    for (i in seq(nrow(col_lut))) {
      col_lut[i,"Reg.abbr"] <- gsub("[:a-z: :blank:]","",toplot$Region[which(toplot$SoilType==col_lut[i,1])[1]])
    }
  }
}
if(col_choice==2) {
    col_lut <- cbind(c(sort(unique(toplot$Region))), c("black", "blue"))
    col_source <- "Region"
}
if(col_choice==3)  { # this will only work for Anu's data
      toplot <- toplot[which(toplot$Region %in% c("NRAM", "ACC")),]
      col_lut <- cbind(c(sort(unique(toplot$SoilType))), c("darkblue", "blue", "red", "darkgoldenrod")) 
      col_source <- "SoilType"
}
if(col_choice==4) { # this will only work for Anu's data
        toplot <- toplot[which(toplot$Region %in% c("OnkoneGare", "Tiputini")),]
        col_lut <- cbind(c(sort(unique(toplot$Region))), c("black"))
        col_source <- "Region"
}
# legend specifications for figures
col_legend <- ifelse(rbind(col_lut[,1])!="", rbind(col_lut[,1]), "Unspecified")
if(col_choice == 5) {
    col_legend <- paste(col_lut[,"Reg.abbr", drop=FALSE], col_legend)
}
if((col_choice==1) | (col_choice==5)) {col_lit <- "black"} else {col_lit <- "red"}
 
# check on screen that the colours are as they should
print(col_lut) 
 
 
 
#### Choose one of the plot types: ####
figure <- "Traplines"  # FIG 1: trap-wise species accumulation lines
figure <- "Estimates"  # FIG 2: plot with estimated number and percentage of unseen species
figure <- "Effort"    # FIG 3: Plot with individuals and spp vs MTM
figure <- "Diversity"  # FIG 4: Plot with richness and diversity vs individuals
figure <- "Latitude"  # FIG 5: Plot with richness against latitude
 
 
#### these plot options are set automatically on the basis of choices made above ####
 
# FIG 1: Trap-wise species accumulation lines
if(figure=="Traplines") {
mfrow <- c(1,3)  # defines the number of panels
hw <- 2.7  # defines the dimensions of each panel
x_own <- cbind(toplot$MTM, toplot$MTM, toplot$Individuals)
xlab <- c("Trap months", "Trap months", "Individuals")
y_own <- cbind(toplot$Individuals, toplot$Species, toplot$Species)
ylab <- c("Individuals", "Species", "Species")
lit <- FALSE
cex_lat <- FALSE
log <- ""
col_pos <- "topleft"
col_panel <- 1
panel_pos <- "bottomright"
}
 
# FIG 2: Plot with estimated number and percentage of unseen species
if(figure=="Estimates") {
mfrow <- c(1,2)  # defines the number of panels
hw <- 4.2  # defines the dimensions of each panel
x_own <- cbind(toplot$Individuals, toplot$Individuals)
xlab <- c("Individuals", "Individuals")
y_own <- cbind(toplot$ChaoAddAbs, toplot$ChaoAddRel)
ylab <- c("Estimated increase in richness (species)", "Estimated increase in richness (%)")
# remove infinite values of ChaoAddRel
  elim <- which(is.na(y_own[,2]))
  if(length(elim > 0)) {
    y_own <- y_own[-elim,]
    x_own <- x_own[-elim,]
    if(cex_lat==TRUE) { cex1 <- cex1[-elim,] }
    col <- col[-elim]
  }
lit <- FALSE
cex_lat <- FALSE
log <- "xy"
col_pos <- "bottomleft"
col_panel <- 2
panel_pos <- "topright"
}
 
# FIG 3: Plot with individuals and spp vs MTM
if(figure=="Effort") {
mfrow <- c(1,2)  # defines the number of panels
hw <- 4.2  # defines the dimensions of each panel
x_own <- cbind(toplot$MTM, toplot$MTM)
xlab <- c("Trap months", "Trap months")
y_own <- cbind(toplot$Individuals, toplot$Species)
ylab <- c("Individuals", "Species")
lit <- TRUE
x_lit <- cbind(litraw$MTM, litraw$MTM)
y_lit <- cbind(litraw$Individuals, litraw$Species)
cex_lat <- TRUE
log <- "xy"
col_pos <- "bottomright"
col_panel <- 1
panel_pos <- "topleft"
}
 
# FIG 4: Plot with richness and diversity vs individuals
if(figure=="Diversity") {
mfrow <- c(1,3)  # defines the number of panels
hw <- 2.9  # defines the dimensions of each panel
x_own <- cbind(toplot$Individuals, toplot$Individuals, toplot$Individuals)
xlab <- c("Individuals", "Individuals", "Individuals")
y_own <- cbind(toplot$Div.q0, toplot$Div.q1, toplot$Div.q2)
ylab <- c("Richness", "Diversity q=1", "Diversity q=2")
lit <- TRUE
x_lit <- cbind(litraw$Individuals)
y_lit <- cbind(litraw$Species)
cex_lat <- TRUE
log <- "xy"
col_pos <- "bottomright"
col_panel <- 2
panel_pos <- "topleft"
}
 
 
 
# colour vector for plotting
if(col_choice == 6)  {col <- "black"}  else {
col <- c(rep("", nrow(toplot)))
for(a in seq(length(col))) col[a] <- col_lut[which(col_lut[,1]==toplot[a,col_source]),2]
}
 
# symbol size set to either constant or scaled by latitude
if(cex_lat == FALSE) {
  cex1 <- cex2 <- 1 } else {
  cex1 <- c(rep(0, dim(toplot)[1]))
  for(a in seq(length(cex1))) cex1[a] <- traptable$Latitude[toplot$Region[a]==traptable$Region][1]
  cex1 <- 2.5*sqrt(cex1/max(abs(litraw$Latitude)))
}
 
# if data are log-transformed, zeroes are removed from the data
if(charmatch("x", log, nomatch=-1)>=0) {
  col <- col[x_own[,2]>0]
  if(cex_lat==TRUE) { cex1 <- cex1[x_own[,2]>0] }
  y_own <- y_own[x_own[,2]>0,]
  x_own <- x_own[x_own[,2]>0,]
}
if(grep("y", log)>0) {
  col <- col[y_own[,2]>0]
  if(cex_lat==TRUE) { cex1 <- cex1[y_own[,2]>0] }
  x_own <- x_own[y_own[,2]>0,]
  y_own <- y_own[y_own[,2]>0,]
}
 
# axis limits
x_lower <- apply(x_own,2,min)
x_upper <- apply(x_own,2,max)
if(figure=="Diversity") {
  y_lower <- rep(min(y_own), dim(y_own)[2])
  y_upper <- rep(max(y_own), dim(y_own)[2])
} else {
  y_lower <- apply(y_own,2,min)
  y_upper <- apply(y_own,2,max)
}
 
# parameters for literature data
################# HUOM! make those sites gray that do not have Rhyssinae data!! ####################
if(lit) {
x_upper <- apply(rbind(apply(x_lit,2,max, na.rm=TRUE), x_upper), 2, max)
y_upper <- apply(rbind(apply(y_lit,2,max, na.rm=TRUE), y_upper), 2, max)
if(cex_lat) {
  cex2 <- 2.5*sqrt(abs(litraw$Latitude)/max(abs(litraw$Latitude)))
} else { cex2 <- 1 }
pch2 <- ifelse(litraw$Elev.max<1000, 1, 19)
}
 
# lettering for the panels
panel <- LETTERS[1:(dim(x_own)[2]*dim(y_own)[2])]
 
# name for the output file
if(log != "") logged <- paste("-log",log,sep="") else logged <- ""
filename <- paste(toplotname,"-", figure, "-lit", lit, logged,
                  "-lat", cex_lat, "-col",col_source, sep="")
#### end of automatic plot options ####
 
 
 
#### Draw the graphs
pdf(file=paste(toplotname, "-", figure, ".pdf", sep=""), height=hw*mfrow[1], width=hw*mfrow[2])
par(mfrow=mfrow, mar=c(4,4,1,1), oma=c(1,1,1,1))
for(i in seq(dim(x_own)[2])) {  # loop through x variables
    plot(x_own[,i], y_own[,i], type="n", log=log,
        xlim=c(if(figure!="Effort") {x_lower[i]} else {x_lower[i]/2}, x_upper[i]),
        ylim=c(y_lower[i], y_upper[i]), xlab=xlab[i], ylab=ylab[i], cex=cex1, col=col)
    title(main=title, cex=0.5, outer=T)
    legend(panel_pos, panel[i], bty="n", cex=1.5)
  if(i==col_panel) {
    if(lit) {
      legend(col_pos, c(col_legend, "Literature"), text.col=c(col_lut[,2], col_lit), bty="n", cex=1)
    } else {
    legend(col_pos, col_legend, text.col=col_lut[,2], bty="n", cex=1)     
    }
  }
if(figure=="Traplines") {
      for(j in seq(unique(toplot$TrapID))) {
      sel_j <- which(toplot$TrapID==unique(toplot$TrapID)[j])
      min_j <- min(toplot[sel_j,"StartDay"])
      sel_j <- intersect(sel_j, which(toplot$StartDay==min(toplot[sel_j,"StartDay"])))
      col1 <- col[sel_j]
      points(x_own[sel_j,i], y_own[sel_j,i], type="l", col=col1)
    }
}
if(figure=="Estimates") {
  points(x_own[,i], y_own[,i], type="p", col=col)
}
if(figure=="Effort" ) {
  points(x_own[,i], y_own[,i], type="p", col=col)
  if(lit) {
    points(x_lit[,i], y_lit[,i], type="p", col=col_lit, cex=cex2, pch=pch2)
}}
if(figure=="Diversity") {
  points(x_own[,i], y_own[,i], type="p", col=col)
  if(lit & i==1) {
    points(x_lit[,i], y_lit[,i], type="p", col=col_lit, cex=cex2, pch=pch2)
  }}
if(figure=="Latitude") {
  points(x_own[,i], y_own[,i], type="p", col=col)
  if(lit & i==1) {
    points(x_lit[,i], y_lit[,i], type="p", col=col_lit, cex=cex2, pch=pch2)
  }}
}
dev.off()
 
##################################### hätäratkaisu
# plot the figure=="Latitude" graph
lat <- 0
for (i in seq(nrow(toplot))) {
  lat <- c(lat, trapraw[which(toplot$Region[i] == trapraw$Region)[1],"Latitude"])}
lat <- as.numeric(lat[-1])
toplot2 <- as.data.frame(cbind("Latitude"=lat, "Species"=toplot$Species, "MTM"=toplot$MTM))
 
pdf(file=paste(toplotname, "-", figure, ".pdf", sep=""), height=4, width=4.8)
par(mfrow=c(1,1), mar=c(4,4,1,1), oma=c(1,1,1,1))
plot(toplot2$Latitude, toplot2$Species, type="n", xlab="Latitude", ylab="Species",
    xlim=c(min(litraw$Latitude), max(litraw$Latitude)), ylim=c(0,max(toplot2$Species)+5))
for(i in seq(unique(toplot2$Latitude))) {
  dots <- toplot2[toplot2$Latitude==unique(toplot2$Latitude)[i],]
  dots2 <- dots[dots$Species<=max(dots$Species)/2,]
  dots2 <- dots2[sample(seq(nrow(dots2)), 10, replace = FALSE, prob = NULL),]
  points(dots2$Latitude, dots2$Species, cex=3*(dots2$MTM/max(litraw$MTM))^(1/3))
  dots2 <- dots[dots$Species>max(dots$Species)/2,]
  dots2 <- dots[sample(seq(nrow(dots2)), 10, replace = FALSE, prob = NULL),]
  points(dots2$Latitude, dots2$Species, cex=3*(dots2$MTM/max(litraw$MTM))^(1/3))
  dots2 <- (dots[dots$Species==max(dots$Species),][1,])
  points(dots2$Latitude, dots2$Species, cex=3*(dots2$MTM/max(litraw$MTM))^(1/3))
}
points(litraw$Latitude, litraw$Species, pch=pch2, cex=3*(litraw$MTM/max(litraw$MTM))^(1/3))
dev.off()
 
#################
 
 
 
########## PART 4: Run and plot NMDS with trapwise data ##########
 
library(vegan)
library(MASS)
 
#### aggregate species data by trap
trapdata <- aggregate(sptable[,-(1:11)], by=list(sptable$TrapID), FUN=sum, simplify=TRUE)
colnames(trapdata)[1] <- "TrapID"
trapdata <- trapdata[match(traptable$TrapID, trapdata$TrapID),]
rownames(trapdata) <- trapdata$TrapID
trapdata <- trapdata[,-1]
 
#### run NMDS using both presence-absence data (Soerensen index) and abundance data (Bray-Curtis relative)
sp.sor <- vegdist(trapdata, method="bray", binary=TRUE) # change here for other dissimilarity
fileout <- paste(spname, "Soerensendist.txt", sep="_")
write.table(as.matrix(sp.sor), file=fileout, sep="\t")
ord_pa <- metaMDS(sp.sor)
sp.bc <- vegdist(decostand(trapdata,"total"), method="bray", binary=FALSE) # change here for other dissimilarity
fileout <- paste(spname, "BrayCurtdist.txt", sep="_")
write.table(as.matrix(sp.bc), file=fileout, sep="\t")
ord_ab <- metaMDS(sp.bc)
 
#### define colors for the plots
col_source <- traptable$SoilType
if(length(unique(col_source))==5) {
  col_lut <- cbind(c(sort(unique(col_source))), c("black", "darkblue", "blue", "red", "darkgoldenrod"))
} else {
  col_lut <- cbind(c(sort(unique(col_source))), c("darkblue", "blue", "red", "darkgoldenrod"))
}
# check that the colors are as they should
print(col_lut) 
col <- c(rep("",length(col_source)))
for(a in seq(length(col))) col[a] <- col_lut[which(col_lut[,1]==col_source[a]),2]
 
# set symbols by Region
pch_source <- traptable$Region
pch_lut <- as.data.frame(cbind(c(sort(unique(pch_source))), c(1, 0, 2, 6)), stringsAsFactors = FALSE)
# check that the symbols are as they should
print(pch_lut) 
pch <- c(rep(0,length(pch_source)))
for(a in seq(length(pch))) pch[a] <- as.numeric(pch_lut[which(pch_lut[,1]==pch_source[a]),2])
 
# plot the ordination result and save it as a pdf file
pdf(file=paste(spname,"NMDS.pdf", sep="_"), height=4, width=8)
par(mfrow=c(1,2), mar=c(4,4,1,1), oma=c(1,1,1,1))
# presence-absence ordination
plot(ord_pa, display="sites", type="n", ylim=c(-0.4,0.4), xlim=c(-0.6, 0.6),
    xlab="Axis 1", ylab="Axis 2")
points(ord_pa, col=col, pch=pch)
legend("topleft", ifelse(rbind(col_lut[,1])!="", rbind(col_lut[,1]),"unspecified"), text.col=col_lut[,2], bty="n", cex=0.7) 
title(main="Presence-absence")
# abundance ordination
plot(ord_ab, display="sites", type="n", ylim=c(-0.4,0.4), xlim=c(-0.6, 0.6),
    xlab="Axis 1", ylab="Axis 2")
points(ord_ab, col=col, pch=pch)
legend("topleft", legend=pch_lut[,1], bty="n", cex=0.7, pch=as.numeric(pch_lut[,2])) 
title(main="Relative abundance")
dev.off()
 
 
########## PART 5: Run and plot NMDS with seasonal data ##########
TARKISTETTAVA
 
library(vegan)
library(MASS)
 
#### aggregate species data by season within trap
sp_data <- sptable[,-5]
sp_data <- na.omit(sp_data)
months <- list(c(12,1,2), c(3:5), c(6:8), c(9:11))
months.names <- c("Dec-Feb", "Mar-May", "Jun-Aug", "Sep-Nov")
for(i in 1:4) {
sp_season <- sp_data[which(sp_data$StartMonth %in% months[[i]]),]
sp_season <- aggregate(sp_season[,-(1:10)], by=list(sp_season$TrapID), FUN=sum, simplify=TRUE)
colnames(sp_season)[1] <- "TrapID"
if(i==1) {
trapdata <- sp_season
season <- rep(months.names[i], nrow(sp_season))
soil <- rep("", nrow(sp_season))
for(k in seq(length(soil))) soil[k] <- traptable[which(trapdata$TrapID[k]==traptable$TrapID),2]
} else {
  trapdata <- rbind(trapdata, sp_season)
  season <- c(season, rep(months.names[i], nrow(sp_season)))
  soil.t <- rep("", nrow(sp_season))
  for(k in seq(length(soil.t))) soil.t[k] <- traptable[which(trapdata$TrapID[k]==traptable$TrapID),2]
  soil <- c(soil, soil.t)
}
}
trap <- trapdata$TrapID
rownames(trapdata) <- paste(season, trapdata$TrapID, sep=".")
trapdata <- trapdata[,-1]
 
min.ind <- 5
season <- season[which(rowSums(trapdata)>=min.ind)]
soil <- soil[which(rowSums(trapdata)>=min.ind)]
trapdata <- trapdata[which(rowSums(trapdata)>=min.ind),]
file <- paste(spname, "_SeasonInd", min.ind, sep="")
 
#### run NMDS using both presence-absence data (Soerensen index) and abundance data (Bray-Curtis relative)
sp.sor <- vegdist(trapdata, method="bray", binary=TRUE) # change here for other dissimilarity
fileout <- paste(file, "Soerensendist.txt", sep="_")
write.table(as.matrix(sp.sor), file=fileout, sep="\t")
ord_pa <- metaMDS(sp.sor)
sp.bc <- vegdist(decostand(trapdata,"total"), method="bray", binary=FALSE) # change here for other dissimilarity
fileout <- paste(file, "BrayCurtdist.txt", sep="_")
write.table(as.matrix(sp.bc), file=fileout, sep="\t")
ord_ab <- metaMDS(sp.bc)
 
#### define colors for the plots
col_source <- list(soil, season)
col_lut <- list()
if(length(unique(col_source[[1]]))==5) {
  col_lut[[1]] <- cbind(c(sort(unique(col_source[[1]]))), c("black", "darkblue", "blue", "red", "darkgoldenrod"))
} else {
  col_lut[[1]] <- cbind(c(sort(unique(col_source[[1]]))), c("darkblue", "blue", "red", "darkgoldenrod"))
}
col_lut[[2]] <- cbind(c(unique(col_source[[2]])), c("black", "green", "blue", "brown"))
# check that the colors are as they should
print(col_lut) 
col <- cbind(rep("",length(col_source[[1]])), rep("",length(col_source[[2]])))
for(a in seq(nrow(col))) {
  col[a,1] <- col_lut[[1]][which(col_lut[[1]][,1]==col_source[[1]][a]),2]
  col[a,2] <- col_lut[[2]][which(col_lut[[2]][,1]==col_source[[2]][a]),2]
}
col_lut[[1]][1,1] <- "undefined"
 
# set symbols by Region
pch_source <- traptable$Region
pch_lut <- as.data.frame(cbind(c(sort(unique(pch_source))), c(1, 0, 2, 6)), stringsAsFactors = FALSE)
# check that the symbols are as they should
print(pch_lut) 
pch <- c(rep(0,length(pch_source)))
for(a in seq(length(pch))) pch[a] <- as.numeric(pch_lut[which(pch_lut[,1]==pch_source[a]),2])
 
 
 
 
 
 
 
############ KESKEN #####################
 
# plot the ordination result and save it as a pdf file
pdf(file=paste(file, "NMDS.pdf", sep="_"), height=7, width=7)
par(mfrow=c(ncol(col),2), mar=c(4,4,1,1), oma=c(1,1,1,1))
for(j in seq(ncol(col))) {
  # presence-absence ordination
plot(ord_pa, display="sites", type="n", xlab="Axis 1", ylab="Axis 2")
points(ord_pa, col=col[,j], pch=pch)
legend("topleft", rbind(col_lut[[j]][,1]), text.col=col_lut[[j]][,2], bty="n", cex=0.7) 
title(main="Presence-absence")
# abundance ordination
plot(ord_ab, display="sites", type="n", xlab="Axis 1", ylab="Axis 2")
points(ord_ab, col=col[,j], pch=pch)
legend("topleft", legend=pch_lut[,1], bty="n", cex=0.7, pch=as.numeric(pch_lut[,2])) 
title(main="Relative abundance")
}
dev.off()
 
</pre>
}}


[[:en:Diversity index|Diversity indices]] are thoroughly described in Wikipedia.
[[:en:Diversity index|Diversity indices]] are thoroughly described in Wikipedia.

Revision as of 18:02, 8 February 2015



Question

How to calculate diversity indices?

Answer

Use the function diversity to calculate the most common indices. These parameters are used:

  • amount: the number (or proportion) of individuals of a species in a transect.
  • species: an identifier for a species
  • transect: an identifier for a transect
  • q: exponent for the diversity calculation


Amount, species, and transect are vectors (i.e. ordered sets of values). The parameters can be given to the function in several different ways. This is hopefully practical for the user.

  • Amount, species, and transect must be of same length.
  • If parameter names are not used (e.g. diversity(param1, param2, param3)), it is assumed that they are given in this order: amount, species, transect, q.
  • If individual data is given using species identifiers, it the parameter name must be given: diversity(species = param1).
  • The following default values are used for parameters:
    • Amount: 1 for each species, i.e. each species is equally abundant.
    • Species: 1, 2, 3, ... n, where n = number of rows in data, i.e. each row is a different species.
    • Transect: 1, i.e. there is only one transect.
    • q: q.wiki. You MUST define an object q.wiki before using diversity, otherwise alpha diversities will be calculated wrong. q.wiki can be a single number or a vector of numbers.


There are several ways to upload your data so that you can use the diversity function.

  • Upload your data to Opasnet Base and call for the data using op_baseGetData function.
  • Upload a CSV file to Opasnet using Special:Upload and call for the data using opasnet.data function.
  • Use Example 1 code below to enter your data. The data must be in format c(4,6,2,5,7,4,3,9) where the values are either
    • identifiers of the species 1,2,3... in which the individuals belong (one entry per individual), or
    • abundancies of species, i.e. proportions or amounts of individuals belonging to each species among the whole population (one entry per species).

Rationale

Actual function diversity

Initiate functions

+ Show code

The function diversity produces a list where the first, second, and third element are the gamma, the alpha, and transect-specific gamma diversities, respectively.

Function diversity.table produces a data.frame of several diversity indices.

Examples

Example 1 to use function

Give your data in R format or leave empty for example data:

Is your data individual data or group abundancies?:

+ Show code

Example 2

Select your data:

Which q values you want to calculate.:
0
0.5
1
2
3
6
∞

+ Show code

The data should be given in R format as a list of values in parenthesis, beginning with c: 

c(3,5,3,5,2,1,3,3,4,2) or equivalently c(0.1,0.2,0.4,0.1,0.2)

where the values are either

  • identifiers of the species 1,2,3... in which the individuals belong (one entry per individual), or
  • abundancies of species, i.e. proportions of individuals belonging to each species among the whole population (one entry per species).

Calculations


Diversity indices are thoroughly described in Wikipedia.

See also

References


Related files

<mfanonymousfilelist></mfanonymousfilelist>

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