Environmental Reduce

In sdStaf: Species Distribution and Stability Future Models

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Define "M" under different ways is matter to calibration models in ecological niche model, we used buffer zone as calibration zone, based on:

• Define M is hard due to species are inhabitants in diferents ecosystem.
• Buffer zone provide a good tools to define calibration area.

Environmental Reduce

These function help you to reduce environmental.

library(sdStaf)
data(phytotoma)

Now, we need to load environmental dataset.

library(dismo)
predictor <- stack(list.files(path=paste(system.file(package="dismo"),'/ex', sep=''), pattern='grd', full.names=TRUE ))

# Read names 
names(predictor)

plot(predictor$bio1)

Next function, reduce environmental data based on buffer zone and customer zone.

buf.M <- stim.M(phytotoma[,2:3], radio = 131)

reduce_cut <- reduce.env(env = predictor, occ_data = phytotoma[,2:3], mask= buf.M)

plot(reduce_cut@cropa$bio1)

points(phytotoma[,2:3], pch=16,col='blue')

We need to show correlogram of predictor variables

 cor.show(reduce_cut)

# Define what variables we need to remove
 rd <- c('bio1','bio12','bio16','biome','bio8')

Remove Rd in reduce_cut, and we have these variables.

cor.show(reduce_cut, rm=TRUE, var.rm = rd)

Define new environmental dataset (no-correlation)

var_reduce <- dropLayer(reduce_cut@cropa, rd)

names(var_reduce)

sdStaf documentation built on May 1, 2019, 8:50 p.m.