Rarefaction Example. Consider this dataset: Original matrix:

Transcription

1 Rarefaction Example Conider thi dataet: Where i diverity highet? S Shannon What about rarefied diverity? rarefy(community,ample=10) am am am am Original matrix: Rarefied matrix rrarefy(community,ample=10) am am am am am am am am Repeated 1,000 time, the average S S 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 Rank abundance Specie Abundance Curve Function: rankabundance, rankabuncomp in the BiodiverityR package rankabundance(community) Can ue either raw abundance or proportion data rankabuncomp allow for comparion among factor rankabunplot will plot the reult Campotoma.anomalum Notropi.boop Etheotoma.pectabile Lepomi.cyanellu Lepomi.megaloti rank_data<-rankabundance(brier_ck) rankabunplot(rank_data) pecie rank

2 Community Similarity or Diimilarity abundance e b d c a a b c d e Community imilarity indice quantify imilarity among two ample. For a full community matrix do all poible pairwie comparion among communitie. Specie pecie 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(brier_ck,y=brier_environmental,factor= year") Community Similarity or Diimilarity 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 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 im_matrix<-vegdit(brier_ck) dim_matrix<-1-im_matrix

3 Quantitative Indice of Similarity (0-1.0) Bray-Curti v PSI ditance for local fih community dataet Ruzicka( PSI) min Bray Curti Both are bound Typically log tranform data P i i1 Where Pi = the proportion of the community compoed of pecie i. ( xij x ) ik x x i1 ij Where x ij = i the abundance of pecie i in community j ik ruzicka bray_curt Quantitative Indice of Similarity (unbound) Euclidian v Manhattan ditance for local fih community dataet Euclidian ( xij xik ) 2 Manhattan= i1 x i1 ij x ik manhattan Typically done without tranformation. Some ue preence/abence matrix with thee metric. No upper limit euclidean

4 Qualitative Indice of Similarity (0-1.0) Steinhau v Sorenen ditance for local fih community dataet 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. h = = 1 (2 + + ) teinhau orenen Bray-Curti v Sorenen ditance for local fih community dataet Defining your own function Function deigndit in the vegan package allow you to define any imilarity index. orenen deigndit(community,method = (B+C)/2, abcd=true) braycurt review of 24 meaure of beta diverity

5 Aignment We will be uing the bee gut microbiome data from the paper read for cla today. The data i available in datadryad: The data i preented in three file Frequency of OTU ( pecie ) by library( ample ) Taxonomic information for each OTU (pecie) Sample information (which treatment, etc.) I did ome filtering and proceing to match what wa written in the paper (eliminated mitochondria and chloroplat, combined the Natural group etc.) The proceed data are given to you a otu_table.cv ample in row by OTU in column ample_meta.cv Factor for colony ID, Population, and Treatment Aignment 1. Eliminate pecie (OTU, the column) with zero occurrence. What i the new total OTU? Doe that match what i in the paper? 2. Calculate the total OTU per ample. What i the minimum of thi among all the ample? Doe thi match what i in the paper? 3. Ue the diveritycomp function to calculate pecie richne, Shannon diverity, and evenne for ample pooled (method= pooled ) among treatment (factor1= Treatment ). 4. Plot a pecie accumulation curve (random method) for the whole dataet. Ue pecaccum function. 5. Calculate a Bray-Curti imilarity matrix for the whole dataet. What i the mean Bray-Curti diimilarity? 6. Create a rarefied matrix uing rrarefy, with the ample ize et to the minimum total number of OTU in a ample (item #2 above). 7. Repeat 3,4, and 5 above with the rarefied matrix. Dicu difference in your ynthei.