NBSI2 {NicheBarcoding}

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

ref.infor

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

que.infor

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

ref.env

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

que.env

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

barcode.identi.result

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

model

Character, string indicating which niche model will be used. Must be one of "RF" (default) or "MAXENT". "MAXENT" can only be applied when the java program paste(system.file(package="dismo"), "/java/maxent.jar", sep=”) exists.

variables

Character, the identifier of selected bioclimate variables. Default of "ALL" represents to use all the layers in en.vir; the alternative option of "SELECT" represents to randomly remove the highly-correlated variables (|r| larger than 0.9) with the exception of one layer.

en.vir

RasterBrick, the global bioclimate data output from "raster::getData" function.

bak.vir

Matrix, bioclimate variables of random background points.

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