R语言vegan包使用教程(9)

R语言中的外在软件包“Vegan”是专门用于群落生态学分析的工具。Vegan能够提供所有基本的排序方法,同时具有生成精美排序图的功能,版本更新很快。我们认为Vegan包完全可以取代CANOCO,成为今后排序分析的首选统计工具。

2ORDINATION:BASICMETHODthistutorialIshowhowtoruntheseanalyseswithveganfunctionsrdaandccawhichactuallyweredesignedforconstrainedanalysis.

Principalcomponentsanalysiscanberunas:

>vare.pca<-rda(varespec)>vare.pca

Call:rda(X=varespec)InertiaRank

Total1826Unconstrained182623Inertiaisvariance

Eigenvaluesforunconstrainedaxes:PC1PC2PC3PC4PC5PC6PC7PC8983.0464.3132.373.948.437.025.719.7

(Showedonly8ofall23unconstrainedeigenvalues)>plot(vare.pca)

Theoutputtellsthatthetotalinertiais1826,andtheinertiaisvari-ance.Thesumofall23(rank)eigenvalueswouldbeequaltothetotalinertia.Inotherwords,thesolutiondecomposesthetotalvarianceintolinearcomponents.Wecaneasilyseethatthevarianceequalsinertia:

>sum(apply(varespec,2,var))[1]1826

Functionapplyappliesfunctionvarorvariancetodimension2orcolumns(species),andthensumtakesthesumofthesevalues.Inertiaisthesumofallspeciesvariances.Theeigenvaluessumuptototalinertia.Inotherwords,theyeach“explain”acertainproportionoftotalvariance.The rstaxis“explains”983/1826=53.8%oftotalvariance.

Thestandardordinationplotcommandusespointsorlabelsforspeciesandsites.Somepeopleprefertousebiplotarrowsforspeciesinpcaandpossiblyalsoforsites.Thereisaspecialbiplotfunctionforthispurpose:

>biplot(vare.pca,scaling=-1)

Forthisgraphwespeci edscaling=-1.Theresultsarescaledonlywhentheyareaccessed,andwecan exiblychangethescalinginplot,biplotandothercommands.Thenegativevaluesmeanthatspeciesscoresaredividedbythespeciesstandarddeviationssothatabundantandscarcespecieswillbeapproximatelyasfarawayfromtheorigin.

Thespeciesordinationlookssomewhatunsatisfactory:onlyreindeerlichens(Cladina)andPleuroziumschreberiarevisible,andallotherspeciesarecrowdedattheorigin.Thishappensbecauseinertiawasvari-ance,andonlyabundantspecieswithhighvariancesareworthexplaining(butwecouldhidethisinplotbysettingnegativescaling).Standard-izingallspeciestounitvariance,orusingcorrelationcoe cientsinsteadofcovarianceswillgiveamorebalancedordination:

>vare.pca<-rda(varespec,scale=TRUE)>vare.pca

9

2.4Eigenvectormethods

6

57

6

4

Cla.ran

Cla.arb

18

132

4

3

2

14

CP0

Cet.nivPol.pilCla.spBar.lycPin.sylCet.eriIch.eriDic.polCla.fimCla.criCla.cerCla.botDic.fusDic.spLed.palCla.corDes.fleCla.chlPoh.nutPol.junCet.islPti.cilEmp.nigVac.myrHyl.spl21Vac.vit2

152212

2

25

24

10Cla.ste

9

4 Ple.sch27

628

4 20246810

PC1

Cla.arbCla.ran

4

Ich.eri

75

Ste.sp62

Vac.uli

Cla.amaPol.pilCla.cocCla.gra18Cal.vul

13Dip.mon2

CCet.eriCet.nivP14Cla.fim430Cla.def20Cla.cer232121522Dic.fus25Pol.jun24

Cla.spPin.syl12Dic.spCet.islCla.chlCla.phy

Led.palCla.ste10

Emp.nig9

2

27Des.fleVac.vitPoh.nut

28

Vac.myr

Hyl.spl

4

Ple.sch

4 20

246

PC1