LappetMoths data set, a list of 8 data frames.

Description

A dataset containing the sequences IDs of species, coordinates of species sampled, and other attributes

Usage

LappetMoths

Format

list of 8 data frames.

barcode.identi.result

data frame,species identifications by other methods or barocodes,containing query IDs, species identified, and corresponding probablities.

que.env

data frame, containing query sampleIDs,and a set of corresponding environmental variables collected by users.

que.infor

data frame, query samples,containing sample IDs,longitude and latitude of each sample.

que.seq

query sequences in binary format stored in a matrix

ref.env

data frame, containing reference sampleIDs, species names, and a set of environmental variables collected by users.

ref.infor

data frame, reference dataset containing sample IDs, taxon information,longitude and latitude of each sample.

ref.seq

reference sequences in binary format stored in a matrix

ref.add

data frame, additional reference dataset containing taxon information, longitude and latitude of each species.

Niche-model-Based Species Identification (NBSI)

Description

Species identification using DNA barcoding integrated with niche model.

Usage

NBSI(
  ref.seq,
  que.seq,
  model = "RF",
  independence = TRUE,
  ref.add = NULL,
  variables = "ALL",
  en.vir = NULL,
  bak.vir = NULL
)

Arguments

Value

A dataframe of barcoding identification result for each query sample and corresponding niche model-based probability.

Author(s)

Cai-qing YANG (Email: yangcq_ivy(at)163.com) and Ai-bing ZHANG (Email:zhangab2008(at)cnu.edu.cn), Capital Normal University (CNU), Beijing, CHINA.

References

Breiman, L. 2001. Random forests. Machine Learning 45(1):5-32.

Liaw, A. and M. Wiener. 2002. Clasification and regression by randomForest. R News, 2/3:18-22.

Phillips, S.J., R.P. Anderson and R.E. Schapire. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190:231-259.

Zhang, A.B., M.D. Hao, C.Q. Yang and Z.Y. Shi. (2017). BarcodingR: an integrated R package for species identification using DNA barcodes. Methods in Ecology and Evolution, 8:627-634.

Jin, Q., H.L. Han, X.M. Hu, X.H. Li, C.D. Zhu, S.Y.W. Ho, R.D. Ward and A.B. Zhang. 2013. Quantifying species diversity with a DNA barcoding-based method: Tibetan moth species (Noctuidae) on the Qinghai-Tibetan Plateau. PloS One, 8:e644.

Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25(15):1965-1978.

Examples

data(en.vir)
data(bak.vir)
#envir<-raster::getData("worldclim",download=FALSE,var="bio",res=2.5)
#en.vir<-raster::brick(envir)
#back<-dismo::randomPoints(mask=en.vir,n=5000,ext=NULL,extf=1.1,
#                          excludep=TRUE,prob=FALSE,
#                          cellnumbers=FALSE,tryf=3,warn=2,
#                          lonlatCorrection=TRUE)
#bak.vir<-raster::extract(en.vir,back)

library(ape)
data(LappetMoths)
ref.seq<-LappetMoths$ref.seq[1:50,]
que.seq<-LappetMoths$que.seq[1:5,]
NBSI.out<-NBSI(ref.seq,que.seq,ref.add=NULL,
               independence=TRUE,
               model="RF",variables="SELECT",
               en.vir=en.vir,bak.vir=bak.vir)
NBSI.out

### Add a parameter when additional reference coordinates are available ###
#ref.add<-LappetMoths$ref.add
#NBSI.out2<-NBSI(ref.seq,que.seq,ref.add=ref.add,
#                independence=TRUE,
#                model="RF",variables="SELECT",
#                en.vir=en.vir,bak.vir=bak.vir)
#NBSI.out2

Niche-model-Based Species Identification (NBSI) for a prior analysis

Description

If users already have species identified by other barcodes or methods, they can use this function given the identified species names and corresponding probabilities to make further confirm by environmental niche model.

Usage

NBSI2(
  ref.infor = NULL,
  que.infor = NULL,
  ref.env = NULL,
  que.env = NULL,
  barcode.identi.result,
  model = "RF",
  variables = "ALL",
  en.vir = NULL,
  bak.vir = NULL
)

Arguments

Value

A dataframe of identifications for query samples and their niche-based reliability.

Author(s)

Cai-qing YANG (Email: yangcq_ivy(at)163.com) and Ai-bing ZHANG (Email:zhangab2008(at)cnu.edu.cn), Capital Normal University (CNU), Beijing, CHINA.

References

Breiman, L. 2001. Random forests. Machine Learning 45(1):5-32.

Liaw, A. and M. Wiener. 2002. Clasification and regression by randomForest. R News, 2/3:18-22.

Phillips, S.J., R.P. Anderson and R.E. Schapire. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190:231-259.

Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25(15):1965-1978.

Examples

data(en.vir)
data(bak.vir)
#envir<-raster::getData("worldclim",download=FALSE,var="bio",res=2.5)
#en.vir<-raster::brick(envir)
#back<-dismo::randomPoints(mask=en.vir,n=5000,ext=NULL,extf=1.1,
#                          excludep=TRUE,prob=FALSE,
#                          cellnumbers=FALSE,tryf=3,warn=2,
#                          lonlatCorrection=TRUE)
#bak.vir<-raster::extract(en.vir,back)

data(LappetMoths)
barcode.identi.result<-LappetMoths$barcode.identi.result
ref.infor<-LappetMoths$ref.infor
que.infor<-LappetMoths$que.infor

if(class(en.vir) == "NULL"){
 NBSI2.out<-NBSI2(ref.infor=ref.infor,que.infor=que.infor,
                  barcode.identi.result=barcode.identi.result,
                  model="RF",variables="SELECT",
                  en.vir=NULL,bak.vir=NULL)
}else{
 NBSI2.out<-NBSI2(ref.infor=ref.infor,que.infor=que.infor,
                  barcode.identi.result=barcode.identi.result,
                  model="RF",variables="SELECT",
                  en.vir=en.vir,bak.vir=bak.vir)
}
NBSI2.out

ref.env<-LappetMoths$ref.env
que.env<-LappetMoths$que.env

NBSI2.out2<-NBSI2(ref.env=ref.env,que.env=que.env,
                  barcode.identi.result=barcode.identi.result,
                  model="RF",variables="ALL",
                  en.vir=en.vir,bak.vir=bak.vir)
NBSI2.out2

bak.vir data set, a class of matrix.

Description

A dataset containing 5 of the 19 bioclimatic variables randomly genereated as background points.

Usage

bak.vir

Format

a class of matrix.

bak.vir

5000*5 matrix.

en.vir data set, a class of RasterBrick.

Description

A dataset containing 5 of the 19 bioclimatic variables downloaded from WorldClim (version 1.4 with 10 arc minute resolution; Hijmans et al. 2005)).

Usage

en.vir

Format

a class of RasterBrick.

en.vir

class: RasterBrick; dimensions : 360, 720, 259200, 5 (nrow, ncol, ncell, nlayers); resolution : 0.5, 0.5 (x, y); extent: -180, 180, -90, 90 (xmin, xmax, ymin, ymax); crs: +proj=longlat +datum=WGS84 +no_defs; source: memory; names: bio1,bio4,bio7,bio12,bio15.

Source

https://www.worldclim.org/

Extraction of taxon/species and distribution information

Description

Split comma-separated sample information into different columns of a data frame.

Usage

extractSpeInfo(seqID.full)

Arguments

Value

A data frame of splitted sample ID, taxon information and longitude and latitude data for further analysis.

Author(s)

Cai-qing YANG (Email: yangcq_ivy(at)163.com) and Ai-bing ZHANG (Email:zhangab2008(at)cnu.edu.cn), Capital Normal University (CNU), Beijing, CHINA.

Examples

data(LappetMoths)
ref.seq<-LappetMoths$ref.seq
seqID.full<-rownames(ref.seq)

infor<-extractSpeInfo(seqID.full)
head(infor)

Calculate the proportion of monophyletic group on a tree

Description

Calculate the proportion of monophyletic group on a tree given species vector and a tree.

Usage

monophyly.prop(phy, sppVector, singletonsMono = TRUE)

Arguments

Value

A list containing proportion and number of monophyly group.

A set monophyly and of non-monophyly group names.

Author(s)

Cai-qing YANG (Email: yangcq_ivy(at)163.com) and Ai-bing ZHANG (Email:zhangab2008(at)cnu.edu.cn), Capital Normal University (CNU), Beijing, CHINA.

Examples

library(ape)
tree<-ape::rtree(20)
tree$tip.label<-sample(tree$tip.label[1:10],size=20,replace = TRUE)
plot(tree)
sppVector<-tree$tip.label

MP<-monophyly.prop(tree,sppVector,singletonsMono = TRUE)
MP

Ecological niche model building using the randomForest classifier

Description

Build a niche model for a given species according to its distribution data.

Usage

niche.Model.Build(
  prese = NULL,
  absen = NULL,
  prese.env = NULL,
  absen.env = NULL,
  model = "RF",
  en.vir = NULL,
  bak.vir = NULL
)

Arguments

Value

randomForest/MaxEnt, a trained niche model object.

A vector including the specificity, sensitivity and threshold of the trained model.

Author(s)

Cai-qing YANG (Email: yangcq_ivy(at)163.com) and Ai-bing ZHANG (Email:zhangab2008(at)cnu.edu.cn), Capital Normal University (CNU), Beijing, CHINA.

References

Breiman, L. 2001. Random forests. Machine Learning 45(1):5-32.

Liaw, A. and M. Wiener. 2002. Clasification and regression by randomForest. R News, 2/3:18-22.

Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25(15):1965-1978.

Examples

data(en.vir)
data(bak.vir)
#envir<-raster::getData("worldclim",download=FALSE,var="bio",res=2.5)
#en.vir<-raster::brick(envir)
#back<-dismo::randomPoints(mask=en.vir,n=5000,ext=NULL,extf=1.1,
#                          excludep=TRUE,prob=FALSE,
#                          cellnumbers=FALSE,tryf=3,warn=2,
#                          lonlatCorrection=TRUE)
#bak.vir<-raster::extract(en.vir,back)

data<-data.frame(species=rep("Acosmeryx anceus",3),
                 Lon=c(145.380,145.270,135.461),
                 Lat=c(-16.4800,-5.2500,-16.0810))
present.points<-pseudo.present.points(data,10,2,1,en.vir)
NMB.out<-niche.Model.Build(prese=present.points,absen=NULL,
                           prese.env=NULL,absen.env=NULL,
                           model="RF",
                           en.vir=en.vir,bak.vir=bak.vir)
NMB.out


prese.env<-raster::extract(en.vir,present.points[,2:3])
prese.env<-as.data.frame(prese.env)
NMB.out2<-niche.Model.Build(prese=NULL,absen=NULL,
                            prese.env=prese.env,absen.env=NULL,
                            model="RF",
                            en.vir=en.vir,bak.vir=bak.vir)
NMB.out2

Principal component analysis of ecological niche among unknown species and the potential species to which they may belong

Description

Determine whether unknown species belong to a known species through principal component analysis of ecological niche according to their distribution information.

Usage

niche.PCA(ref.lonlat, que.lonlat, en.vir = NULL)

Arguments

Value

A list containing inportance and loadings of the components.

A dataframe of points that within the 95% confidence interval of the reference dataset ecological space.

A figure shows whether the query points (blue solid circles) are located in the 95%CI range of the niche space of reference species.

Author(s)

Cai-qing YANG (Email: yangcq_ivy(at)163.com) and Ai-bing ZHANG (Email:zhangab2008(at)cnu.edu.cn), Capital Normal University (CNU), Beijing, CHINA.

Examples

data(en.vir)
#envir<-raster::getData("worldclim",download=FALSE,var="bio",res=2.5)
#en.vir<-raster::brick(envir)

data(LappetMoths)
ref.infor<-LappetMoths$ref.infor
que.infor<-LappetMoths$que.infor

#windows() # open a new plot window when the image format is abnormal
nPCA<-niche.PCA(ref.lonlat=ref.infor[,3:5],
                que.lonlat=que.infor[,c(2,4:5)],
                en.vir=en.vir)
nPCA$summary
nPCA$que.CI


data<-data.frame(species=rep("Acosmeryx anceus",3),
                 Lon=c(145.380,145.270,135.461),
                 Lat=c(-16.4800,-5.2500,-16.0810))
simuSites<-pseudo.present.points(data,500,4,2,en.vir)
ref.lonlat<-simuSites[1:480,]
que.lonlat<-simuSites[481:500,]

#windows() # open a new plot window when the image format is abnormal
nPCA2<-niche.PCA(ref.lonlat,que.lonlat,en.vir=en.vir)
nPCA2$summary
nPCA2$que.CI

Generation of pseudo absent points for niche model building

Description

Randomly generate pseudo points outside the 95%CI of the ecological space of the present data when there is no absent data for building a niche model.

Usage

pseudo.absent.points(data, outputNum = 500, en.vir = NULL, map = TRUE)

Arguments

Value

A data frame of simulated pseudo points.

A data frame of bioclimate variables of each pseudo points.

Author(s)

Cai-qing YANG (Email: yangcq_ivy(at)163.com) and Ai-bing ZHANG (Email:zhangab2008(at)cnu.edu.cn), Capital Normal University (CNU), Beijing, CHINA.

Examples

data(en.vir)
#envir<-raster::getData("worldclim",download=FALSE,var="bio",res=2.5)
#en.vir<-raster::brick(envir)

data<-data.frame(species=rep("Acosmeryx anceus",3),
                 Lon=c(145.380,145.270,135.461),
                 Lat=c(-16.4800,-5.2500,-16.0810))

absent.points<-pseudo.absent.points(data,en.vir=en.vir,outputNum=100)
head(absent.points$lonlat)
head(absent.points$envir)

Generation of pseudo present points for niche model building

Description

Randomly generate pseudo points around actual present distribution site when the number of present points is inadequate for building a niche model.

Usage

pseudo.present.points(
  data,
  outputNum = 50,
  lonRange = 2,
  latRange = 1,
  en.vir = NULL,
  map = TRUE
)

Arguments

Value

A data frame, containing actual present points and simulated pseudo points.

Author(s)

Cai-qing YANG (Email: yangcq_ivy(at)163.com) and Ai-bing ZHANG (Email:zhangab2008(at)cnu.edu.cn), Capital Normal University (CNU), Beijing, CHINA.

Examples

data(en.vir)
#envir<-raster::getData("worldclim",download=FALSE,var="bio",res=2.5)
#en.vir<-raster::brick(envir)

data<-data.frame(species=rep("Acosmeryx anceus",3),
                 Lon=c(145.380,145.270,135.461),
                 Lat=c(-16.4800,-5.2500,-16.0810))


present.points<-pseudo.present.points(data,10,2,1,en.vir=en.vir)
present.points

Mantel test between interspecific pairwise genetic distance and ecological distance

Description

Determine the independence between genetic distance and ecological distance for a reference dataset at the level of species.

Usage

spe.mantel.test(
  fas,
  dna.model = "raw",
  ecol.dist.method = "euclidean",
  mantel.method = "spearman",
  permutations = 999,
  en.vir = NULL
)

Arguments

Value

The Mantel statistic.

The empirical significance level from permutations.

A matrix of interspecific pairwise genetic distance.

A matrix of interspecific pairwise ecological distance.

Author(s)

Cai-qing YANG (Email: yangcq_ivy(at)163.com) and Ai-bing ZHANG (Email:zhangab2008(at)cnu.edu.cn), Capital Normal University (CNU), Beijing, CHINA.

References

Mantel N. 1967. The detection of disease clustering and a generalized regression approach. Can. Res. 27:209-220.

Oksanen J., F.G. Blanchet, M. Friendly, R. Kindt, P. Legendre, D. McGlinn, P.R. Minchin, R.B. O'Hara, G.L. Simpson, P. Solymos, M.H.H. Stevens, E. Szoecs and H Wagner. 2016. vegan: Community Ecology Package https://CRAN.R-project.org/package=vegan. r package version 2.5-6.

Examples

data(en.vir)
#envir<-raster::getData("worldclim",download=FALSE,var="bio",res=2.5)
#en.vir<-raster::brick(envir)

library(ape)
data(LappetMoths)
ref.seq<-LappetMoths$ref.seq

spe.mantel<-spe.mantel.test(fas=ref.seq,en.vir=en.vir)
spe.mantel$MantelStat.r
spe.mantel$p.value