humboldt.grid.espace: Create a grid of environmental space
In jasonleebrown/humboldt: Analysis of Species in Environmental Space

humboldt.grid.espace

R Documentation

Create a grid of environmental space

Description

Create a grid of environmental space

Usage

humboldt.grid.espace(glob.g, glob.s, sp, R = 100, kern.smooth = 1)

Arguments

`glob.g`	pca values in 2 dimensions for the whole study area of both species
`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 uses the scores of principal components (or 2 environmental variables) and creates a grid of RxR pixels with occurrence densities for species input.

Only two dimensions can be used. If performing a PCA, I strongly encourage you to run a species distribution model using lots of environmental data on the focal species and then load only the top contributing environmental variables to be included in the PCA. This way all included variables are known to be relevant in both species distributions. This can be done inside Humboldt (via humboldt.top.env) by importing many environmental variables and letting program select only those important. Alternatively, this can also be done using another method (MaxEnt) outside of R and then use only variables deemed important in the species' distributions.

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"))

##convert geographic space to espace
zz<-humboldt.g2e(env1=env1, env2=env2, sp1=occ.sp1, sp2=occ.sp2, reduce.env = 2, reductype = "PCA", non.analogous.environments = "NO", e.var=c(3:21),  col.env = e.var, env.trim= T, env.trim.type="MCP", 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]

## run Create a grid of Environmental Space Function
z1<- humboldt.grid.espace(scores.env12,scores.env1,scores.sp1,kern.smooth=1,R=100)
z2<- humboldt.grid.espace(scores.env12,scores.env2,scores.sp2,kern.smooth=1,R=100)

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