2/1/2016. Species Abundance Curves Plot of rank abundance (x-axis) vs abundance or P i (yaxis).
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1 Specie Abundance Curve Plot of rank abundance (x-axi) v abundance or P i (yaxi). More divere communitie lack numerically dominant pecie, flatter line. Proportion abundance A C DB F E A B C D E F abundance pecie rank pecie rank Rank # Compare rank abundance among creek or year # THIS FUNCTION WILL WAIT FOR YOU TO CLICK ON THE GRAPH TO PLACE THE LEGEND rank_data<-rankabuncomp(community,y=ample_factor,factor="creek") # Compare rank abundance among creek or year # THIS FUNCTION WILL WAIT FOR YOU TO CLICK ON THE GRAPH TO PLACE THE LEGEND rank_data<-rankabuncomp(community,y=ample_factor,factor="year") Specie Abundance Curve Function: rankabundance, rankabuncomp in the BiodiverityR package rankabundance(community,y=environmental,factor) Can ue either raw abundance or proportion data rankabuncomp allow for comparion among factor rankabunplot will plot the reult Baic Plotting Function plot doe a baic catter plot. plot(loblolly$age, Loblolly$height) abundance Specie2 Specie10 Specie4 Specie5 Specie7 rank_data<-rankabundance(community) rankabunplot(rank_data) pecie rank 1
2 Baic Plotting You can cutomize: Axi label with xlab or ylab variable Symbol with pch variable Symbol color with col variable Symbol ize with cex variable Heatmap Heatmap provide a quick way to viualize data. Function heatmap will plot raw value in a matrix: heatmap(a.matrix(community),rowv=na,colv=na) Can only plot a matrix, not a data frame. Ue a.matrix to convert, auming everything in your data frame i numeric. plot(loblolly$age,loblolly$height,xlab="age",ylab="height",pch=15,col="blue",cex=1.5) Baic Plotting Plot function will behave differently if one of the variable in a factor. Note pacing on a-axi. plot(a.factor(loblolly$age),loblolly$height,xlab="age",ylab="height",col="blue",cex=1. 5, cex.lab=1.5, cex.axi=1.5) Community Similarity or Diimilarity Community imilarity indice quantify imilarity among two ample. For a full community matrix do all poible pairwie comparion among communitie. Specie Sample Sample Sample 2
3 Quantitative Indice of Similarity (0-1.0) Symmetrical v. aymmetrical metric Are hared zero indicator of actual imilarity? Community data typically ue aymmetric metric Qualitative v. quantitative Ordinal v categorical, bionmical etc. Q mode v. R mode Q: Are actual object being compared (quetion i how imilar are A and B) E.g.: how imilar i community A and B R: Are relationhip or dependence of meaure of interet (quetion i if A i correlated with B) E.g. I pecie X abundance correlated with temperature Ruzicka( PSI) min Bray Curti Both are bound Typically log tranform data P i Where Pi = the proportion of the community compoed of pecie i. ( xij x ) ik x x ij Where x ij = i the abundance of pecie i in community j ik Community Similarity or Diimilarity Function: vegdit (vegan package), dvdi (labdv package), daiy (cluter package), deigndit(vegan) Very often raw abundance data can be ued Variable depending on propertie of metric, look at how each i calculated Mot are bound (0-1 range) Converion to imilarity or diimilarity Sim_matrix<-vegdit(community) Dim_matrix<-1-Sim_matrix ruzicka Bray-Curti v PSI ditance for local fih community dataet bray_curt 3
4 Quantitative Indice of Similarity (unbound) Qualitative Indice of Similarity (0-1.0) Euclidian ( xij xik ) 2 Manhattan= x Typically done without tranformation. Some ue preence/abence matrix with thee metric. No upper limit. ij x ik Where a = number of pecie in both communitie, b= number of pecie unique to community 1, c = number of pecie unique to community 2 Both convert data to preence/abence. Can be converted to diimilarity by 1-Steinhau or Sorenen. Steinhau a a b c 2a Sorenen 1 2( a b c) Euclidian v Manhattan ditance for local fih community dataet Steinhau v Sorenen ditance for local fih community dataet manhattan teinhau euclidian orenen 4
5 Bray-Curti v Sorenen ditance for local fih community dataet orenen Genetic Ditance A variety of package will work with genetic data and calculate ditance matrice. Package adegenet ha a function dit.genpop to calculate five common meaure (?dit.genpop for detail) # genetic ditance library(adegenet) #load a microat dataet that come with the package data(microatt) # table of allele frequencie (18 population x 112 allel) microatt$tab dim(microatt$tab) # euclidean ditance mat<-euclidean<-dit(microatt$tab) # Nei' ditance nei<-dit.genpop(a.genpop(microatt$tab), method=1) plot(mat,nei) INRA5.133 INRA5.137 INRA5.139 INRA5.141 INRA5.143 INRA Baoule Borgou BPN Charolai Holtein Jerey Lagunaire Limouin MaineAnjou Mtbeliard NDama Normand Parthenai Somba Vogienne ZChoa ZMbororo Zpeul bray_curt Defining your own function Function deigndit in the vegan package allow you to define any imilarity index. mat<-euclidean<-dit(microatt$tab) # Nei' ditance nei<-dit.genpop(a.genpop(microatt$tab), method=1) plot(mat,nei) heatmap(a.matrix(nei),rowv=t,colv=t) deigndit(community,method = (B+C)/2, abcd=true) review of 24 meaure of beta diverity 5
6 Heat Map of imilarity indice Unordered diimilarity (braycurti) heatmap(x=a.matrix(braycurt),rowv=na,colv=na) Ordered diimilarity (braycurti) with dendrogram in the margin heatmap(x=a.matrix(braycurt), Rowv=T,Colv=T) New function thi week pecaccum diverityreult vegdi, dvdi, deigndi rankabundance, rankabuncomp, rankabunplot plot heatmap dit.genepop RColorBrewer RColorBreer i a package that ha nicer palate of color for thing like heat map. library(rcolorbrewer) diplay.brewer.all(type="eq") heatmap(x=a.matrix(braycurt),col = brewer.pal(9,"purd"), Rowv=T,Colv=T) Aignment Ue the ame dataet a lat week (Fun_08_09_community.cv) Load data Eliminate rare pecie (any pecie with le than three occurrence) Eliminate ample with le than 50 individual. Calculate the etimated rarefied richne (and tandard deviation of the etimate) at 20 ample. Produce a rank abundance plot for the community (all pecie and all ample) With rare pecie and low abundance ample eliminated, calculate Bray Curti and Steinhau imilarity indice. Preent the Bray Curti imilarity a a heatmap uing any color cheme except the default. 6
Rarefaction Example. Consider this dataset: Original matrix:
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