MaxEnt: Prediction and cross-validation using the Maximum Entropy

Description

Runs MaxEnt algorithm on a set of observations ("ppp" class from the spatstat package) and environmental covariates (of "SpatialPixelsDataFrame" class) and returns predicted probability of occurrence and cross-validation of models with presence/absence data.

Usage

"MaxEnt"(occurrences, covariates, nfold = 5, Npoints = 1000, sciname = as.character(NA),  period = c(Sys.Date()-1, Sys.Date()), ...)

Arguments

occurrences

object of type "ppp"; occurrences

covariates

object of type "SpatialPixelsData"; list of covariate layers

nfold

object of type "integer"; number of folds used for cross-validation

Npoints

object of type "integer"; number of points used for cross-validation

sciname

object of type "character"; usually species latin name (it can also be a surveyor's team name or a sampling design)

period

object of type "Date"; sampling period

...

for more additional arguments see dismo::predict

Value

"SpatialMaxEntOutput" with the following slots: sciname (usually latin "genus" and "species" name), occurrences (occurrence-only records), TimeSpan.begin (begin of sampling), TimeSpan.end (end of sampling), maxent (object of class "MaxEnt" produced as an output of the dismo::maxent function), sp.domain (assumed spatial domain), and predicted (results of prediction produced using the MaxEnt software).

References

Phillips, S.J., Anderson, R.P., Schapire, R.E., (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190:231-259.
MaxEnt software (http://www.cs.princeton.edu/~schapire/maxent/)
Dismo package (http://CRAN.R-project.org/package=dismo)

Examples

Run this code

# load data:
library(plotKML)
library(rJava)
library(spatstat)
library(maptools)
library(dismo)
library(rgdal)

data(eberg)
data(eberg_grid)
## prepare data for spatial analysis:
eberg.xy <- eberg[runif(nrow(eberg))<0.3,]
coordinates(eberg.xy) <- ~X+Y
proj4string(eberg.xy) <- CRS("+init=epsg:31467")
## format gridded data:
gridded(eberg_grid) <- ~x+y
proj4string(eberg_grid) <- CRS("+init=epsg:31467")
## convert to a "ppp" object:
eberg.ppp <- as.ppp(eberg.xy)
## run MaxEnt analysis (evaluates sampling bias or mis-representation):
jar <- paste(system.file(package="dismo"), "/java/maxent.jar", sep='')
if(file.exists(jar)){
  me.eberg <- MaxEnt(occurrences=eberg.ppp, covariates=eberg_grid)
  ## NOTE: MaxEnt can be time-consuming!
  # plot the results:
  par(mfrow=c(1,2), mar=c(0.5,0.5,0.5,0.5), oma=c(0,0,0,0))
  image(as(me.eberg@predicted, "SpatialPixelsDataFrame"), 
    col=rev(heat.colors(25)), xlab="", ylab="")
  points(me.eberg@occurrences, pch="+", cex=.7)
  image(me.eberg@sp.domain, col="grey", xlab="", ylab="")
}