R语言vegan包计算多样性 r语言计算多样性指数

文章目录

• Alpha 多样性指数
• Beta多样性指数
• Hill的Beta多样性指数和Gamma多样性指数
• 基于距离和方差分解的Beta多样性指数
• 功能多样性
• 系统发育多样性

Alpha 多样性指数

常见的Alpha多样性指数

R代码如下：

### library(vegan)   ##加载群落数据分析常用的包
data(BCI)  ##vegan包中自带的群落数据

shannon <- diversity(BCI,MARGIN = 1)  ## Shannon 多样性指数， MARGIN=1 计算行的多样性 ，一般的群落数据表行名为样方名，列名为物种名  index默认为“shannon”

simpson <- diversity(BCI,index = "simpson",MARGIN = 1)   ## Simpson多样性指数

SR <- specnumber(BCI,MARGIN = 1)    ## 物种丰富度：相当于每一行大于0 (生物量、盖度或多度) 的物种数

Pielou <- shannon/log(SR)     ##均匀度指数

diversity <- data.frame(shannon,simpson,SR,Pielou)  ##将物种丰富度、Shannon多样性指数、Simpson多样性指数和Pielou' 均匀度指数放置在一个数据框中

write.csv(diversity,"diversity.csv")  ## 将多样性结果导出为csv文件

Beta多样性指数

Hill的Beta多样性指数和Gamma多样性指数

library(vegan)
library(vegetarian)

data(dune)

d(dune,lev = "alpha",q=0) ## 平均alpha 物种丰富度

d(dune,lev = "alpha",q=1) ## 平均alpha Shannon多样性

d(dune,lev = "alpha",q=2) ## 平均alpha Simpson多样性

d(dune,lev = "beta",q=0) ##倍性beta物种丰富度

d(dune,lev = "beta",q=1) ##倍性beta Shannon多样性

d(dune,lev = "beta",q=2) ##倍性beta Simpson多样性

d(dune,lev = "gamma",q=0) ##Gamma物种丰富度

d(dune,lev = "gamma",q=1) ##Gamma Shannon多样性

d(dune,lev = "gamma",q=2) ##Gamma Simpson多样性

基于距离和方差分解的Beta多样性指数

library(vegan)
betadiver(dune,method = "w") ## The basic Whittaker index
betadiver(help=TRUE)  ## 共有24中计算Beta多样性（距离）的方法

library(adespatial)

dune.beta <- beta.div(dune,method = "hellinger",nperm = 999)
dune.beta$beta
dune.beta$SCBD   ##  大于平均值的物种
dune.beta$LCBD   ##Beta 多样性样方贡献

功能多样性

单性状（混合性状）功能多样性

data(dummy)
ex1 <- dbFD(dummy$trait, dummy$abun)  ##混合性状或单一性状的功能多样性
ex1

多性状多样方功能多样性

library(tidyverse)
library(FD)
##  制作空数据框用于后续存放数据

Plot<- 'abc'
trait1<-0
trait2<-0
trait3<-0
trait4<-0
trait5<-0
trait6<-0
trait7 <- 0
trait8 <- 0
CWM.cover.result <- data.frame(Plot,trait1,trait2,trait3,trait4,trait5,trait6,trait7,trait8)
CWM.cover.result$Plot <- as.character(CWM.cover.result$Plot)
str(CWM.cover.result)
FDis.cover.result <- CWM.cover.result

Trait <- c('trait1','trait2','trait3','trait4','trait5','trait6','trait7','trait8')
Plot <- unique(trait_mean$Plot)

#用循环来计算每个样方每种性状的功能多样性
#cycle
for (i in 1:8) {
  for (v in 1:72) {
    
    Trait.i <- Trait[i]   ##  第i个性状
    Plot.v <- Plot[v]     ##  第v个样方
    
    Data.i <- trait_mean%>%     ##  选择性状数据  
      dplyr::select('Plot','Species',Trait.i)%>%
      filter(Plot==Plot.v)
    
    names(Data.i)<-c('Plot','Species','Trait_i')
    Data.i<-Data.i%>%  
      filter(Trait_i != 'NaN')
    
    Cover<-cover%>%            ##  选择群落数据（这里使用盖度数据）  长数据
      filter(Species %in% unique(Data.i$Species))%>%
      filter(Plot==Plot.v)
    Data.i<- Data.i%>%
      filter(Species %in% unique(Data.i$Species))
    
    data.db <-merge(Data.i,Cover,by=c('Plot','Species'))%>%  ## 将两种数据组合
      filter(Cover != 0)
    
    data.db.trait <- dplyr::select(data.db,Species,Trait_i)
    row.names(data.db.trait) <- data.db.trait$Species
    data.db.trait<-subset(data.db.trait,select = Trait_i)
    
    data.db.cover <- dplyr::select(data.db,Species,Cover)%>%  ## 将盖度长数据转化为宽数据
      spread(key=Species,value=Cover)
    
    CWM <- dbFD(x=data.db.trait, a=data.db.cover, calc.FRic = F, calc.FDiv = F, w.abun = T, calc.CWM = TRUE)   ## 计算功能多样性，这里主要计算了FDis和CWM
    
    CWM.cover.result[v,1] <- Plot.v
    CWM.cover.result[v,i+1] <- CWM$CWM[1,1]
    
    FDis.cover.result[v,1] <- Plot.v
    FDis.cover.result[v,i+1] <- CWM$FDis
    
  }
  
}


CWM.cover.result <- CWM.cover.result%>%  ## 将结果转化为长数据格式并重命名
  gather(key=key,value = value,-Plot)%>%
  mutate(CWM='CWM')%>%
  unite(key2,CWM,key,sep="_")%>%
  spread(key=key2,value = value)

FDis.cover.result <- FDis.cover.result%>%  ## 将结果转化为长数据格式并重命名
  gather(key=key,value = value,-Plot)%>%
  mutate(CWM='FDis')%>%
  unite(key2,CWM,key,sep="_")%>%
  spread(key=key2,value = value)

系统发育多样性

com_2020 <- read.csv("com2020.csv",row.names = 1)  ## 读取群落数据
library(ape)
library(picante)                                   ## 加载系统发育分析包
phylo <- read.tree("tree.treefile")                ## 读取系统发育树文件
rooted <- root(phylo,outgroup = "Amborella_trichopoda",resolve.root = T)  ## 转化为有根树

a <- cophenetic(rooted)%>%as.data.frame()          ## 转化为距离矩阵

cover2020.pd<-pd(com_2020,rooted)                  ## 求Fish' 系统发育距离

cover2020.mpd.ab<-mpd(com_2020,a,abundance.weighted = FALSE)  ##  平均系统发育距离

cover2020.mntd.ab<-mntd(com_2020,a,abundance.weighted = FALSE) ##  平均最近种系统发育距离

cover2020.mpd.ab<-mpd(com_2020,a,abundance.weighted = TRUE)  ##  平均系统发育距离 (盖度加权)

cover2020.mntd.ab<-mntd(com_2020,a,abundance.weighted = TRUE) ##  平均最近种系统发育距离 (盖度加权)


cover2020.ses.mpd.pa<-ses.mpd(com_2020,a,abundance.weighted = FALSE)  ##  标准化平均系统发育距离  物种池为系统发育树物种池

cover2020.ses.mntd.pa<-ses.mntd(com_2020,a,abundance.weighted = FALSE) ##  标准化平均最近种系统发育距离

cover2020.ses.mpd.pa<-ses.mpd(com_2020,a,abundance.weighted = FALSE) ##  标准化平均系统发育距离 (盖度加权)

cover2020.ses.mntd.pa<-ses.mntd(com_2020,a,abundance.weighted = FALSE)  ##  标准化平均最近种系统发育距离 (盖度加权)

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