humboldt.pnt.index: Measure potential niche truncation index

In jasonleebrown/humboldt: Analysis of Species in Environmental Space

Measure potential niche truncation index

Description

Measure potential niche truncation index

Usage

humboldt.pnt.index(glob.g, glob.s, sp, R = 100, kern.smooth = 1)

Arguments

glob.g	pca values in 2 dimensions for the whole study area
glob.s	pca values in 2 dimensions for the range of species
sp	pca values in 2 dimensions for the occurrences of the species in the ordination.
R	resolution of grid in environmental space (RxR)
kern.smooth	scale at which kernel smoothing occurs on environmental data, larger values (i.e. 2) increase scale (making espace transitions smoother and typically larger) and smaller values (i.e. 0.5) decrease scale (making occupied espace clusters more dense and irregular). Default value is 1. You can also input: "auto", which estimates the kernel parameter by calculating the standard deviation of rescaled PC1 and PC2 coordinates divided by the sixth root of the number of locations. This method can be unreliable when used on multimodal espace distributions as it results in over-smoothing of home ranges. Multimodel espace occupancy can be somewhat common when a species occupies an extreem aspect of habitat or when espace is not broadly acessible in both dimensions of espace (PCs 1 & 2).

Value

This tool estimates the Potential Niche Truncation Index, stored as the "pnt.index". The value describes the amount of observed E-space of the species that is truncated by available E-space. It is a measurement of the overlap between the 5 percent Kernel Density isopleth of the species and the 10 percent Kernel Density isopleth of accessible environment E-space. The value is the portion of the species’ isopleth that falls outside of the environmental isopleth. This value physically measures how much of the perimeter of the species' E-space abuts or overlaps with the margins of the environment’s E-space. If the PNT index is moderate or high, keep in mind that your realized niche likely does not adequately reflect the species' fundamental niche.

See Also

humboldt.equivalence.stat, humboldt.background.stat, humboldt.niche.similarity, humboldt.plot.niche,humboldt.doitall, humboldt.top.env that use or depend on outputs of this function

Examples

library(humboldt)

##load environmental variables for all sites of the study area 1 (env1). Column names should be x,y,X1,X2,...,Xn)
env1<-read.delim("env1.txt",h=T,sep="\t")

## load environmental variables for all sites of the study area 2 (env2). Column names should be x,y,X1,X2,...,Xn)
env2<-read.delim("env2.txt",h=T,sep="\t") 

## remove NAs and make sure all variables are imported as numbers
env1<-humboldt.scrub.env(env1)
env2<-humboldt.scrub.env(env2)

##load occurrence sites for the species at study area 1 (env1). Column names should be 'sp', 'x','y'
occ.sp1<-na.exclude(read.delim("sp1.txt",h=T,sep="\t"))

##load occurrence sites for the species at study area 2 (env2). Column names should be 'sp', 'x','y'. 
occ.sp2<-na.exclude(read.delim("sp2.txt",h=T,sep="\t"))

##its highly recommened that you using the function "humboldt.top.env" to select only the important enviromnetal variables. This step can be skipped. If you downloaded tons of environmental data, you should use this step.  If you skip this step, input env1/env2 inplace of reduc.vars$env1/reduc.vars$env2 
reduc.vars<- humboldt.top.env(env1=env1,env2=env2,sp1=occ.sp1,sp2=occ.sp2,rarefy.dist=40, rarefy.units="km", env.reso=0.416669,learning.rt1=0.01,learning.rt2=0.01,e.var=(3:21),pa.ratio=4,steps1=50,steps2=50,method="contrib",contrib.greater=5)

##Adjust the number of variables input for e.vars after reduction to only important variables
num.var.e<-ncol(reduc.vars$env1)

##convert geographic space to espace for measuring pnt.index
zz<-humboldt.g2e(env1=reduc.vars$env1, env2=reduc.vars$env2, sp1=occ.sp1, sp2=occ.sp2, reduce.env = 0, reductype = "PCA", non.analogous.environments = "YES", env.trim= T, e.var=c(3:num.var.e),  col.env = e.var, trim.buffer.sp1 = 200, trim.buffer.sp2 = 200, rarefy.dist = 50, rarefy.units="km", env.reso=0.41666669, kern.smooth = 1, R = 100, run.silent = F)

##store espace scores for sp1 and environments 1,2 and both environments combined output from humboldt.g2e
scores.env1<-zz$scores.env1[1:2]
scores.env2<-zz$scores.env2[1:2]
scores.env12<- rbind(zz$scores.env1[1:2],zz$scores.env2[1:2])
scores.sp1<-zz$scores.sp1[1:2]
scores.sp2<-zz$scores.sp2[1:2]

## estimate the Potential Niche Truncation Index
pnt1<- humboldt.pnt.index(scores.env12,scores.env1,scores.sp1,kern.smooth=1,R=100)
pnt2<- humboldt.pnt.index(scores.env12,scores.env2,scores.sp2,kern.smooth=1,R=100)

jasonleebrown/humboldt documentation built on Jan. 4, 2024, 7:46 a.m.