Ecological niche modelling in R - part 1

23 Nov 2016

There are severl ENM/SDM packages in literature, but dismo is the very first and very useful package that include a lot of basic functions in ENM. Other packages may help you automatically do a lot works, but dismo is the one that help you understand what is going on underneath process of data manupulation & niche modeling. Personally, I think dismo is a great tool for methodological studies if your study involves parameter manipulations or a lot of repeated workflows (e.g. a lot of species).

# Load dismo library
## Loading required package: methods
## Loading required package: raster
## Loading required package: sp

1. occurrences

1.1 Load occurrences

Typically, there are two ways to get occurrences: 1) load a local csv file into R; 2) download observations from online databases, e.g. GBIF . There is a gbif() function can help automatically download species’ occurrences without clicking or searching on GBIF website. The parameters provided by gbif() give you a lot flexibilities. See this simple example below.

#simply type in the species' scientific name
 occ <- gbif("Dasypus novemcinctus")

You may also automatically go over a list of species you are interested.

species_list <- c("Dasypus kappleri",
                  "Dasypus hybridus",
                  "Dasypus novemcinctus")
for (i in 1:length(species_list)){
   occ <- gbif( species_list[i] )  # download i-th species and save data

Most commonly, we load a csv file from local disk.

occ <- read.csv("d:/temp/Dasypus_novemcinctus.csv")

1.2 Manipulate occurrences

We always need to clean the occurrences. Here I showed a few functions frequently used for my work.

Example of removing duplicated records according to the columns of lon & lat.

# check duplicates
dup_results <- duplicated(occ[c("lon","lat")])
table(dup_results)  # TRUE: not unique record, FALSE: unique record
## dup_results
##  1733  2630
nrow(occ)  # original records number
## [1] 4363
occ_unique <- occ[!dup_results,]
nrow(occ_unique)  # updated records number
## [1] 1733

Example of subseting records by attributes (column).

# remove NA values 
occ_updated <- subset(occ_unique, (! | (! )

# select by year
occ_selected <- subset(occ_unique,year==2016)  # select records in 2016

occ_selected <- subset(occ_unique,year>=1950 & year <=2000)  # between 1950~2000

occ_selected <- subset(occ_unique,year<1950 | year >2000)  # before 1950 or after 2000

occ_selected <- subset(occ_unique,year>=1950  # a combination of more conditions
                       & year <=2000 
                       & lat > 0)

1.3 Make occurrences spatial!

Pay attention to the grammar.
(1) Use <- ~, not <-.
(2) “lon” and “lat” are column names from the data.frame.
(3) make sure x/longitude first.

coordinates( occ_updated ) <- ~ lon + lat
## [1] "SpatialPointsDataFrame"
## attr(,"package")
## [1] "sp"

With R, we can easily transform spatial data into shape files, which may be used in ArcGIS or QGIS for plotting purpose.

occ_updated <- shapefile("d:/temp/occ.shp")

2. Raster files

I used worldclim dataset as an example. Please refer to my GIS Tutorial for more examples of raster analysis in R.

# load two layers: annual T. & P.
basemap <- stack(c("d:/Xiao/GISDATA/climate/WC/WorldClim_10arcmin_bil/bio1.bil"),

# Extract the raster to North & South America
extent <- c(-180,-30,-60,80)
basemap <- crop(basemap,extent) 

plot of chunk unnamed-chunk-10

Plot occurrences on the base map.

# It is always good to unify the coordinate reference system (CRS). There is a good document about CRS 
crs(occ_updated) <- crs(basemap)  # Here I directly assign WGS84 to occurrences; but if you have different CRS, you need to transform one to the other.

plot of chunk unnamed-chunk-11

It seems some points are not located on the landmass, which should have NA values. Let’s remove them.

extracted <- extract(basemap[[1]],occ_updated)
table(  # There are a few points have NA values
##  1729     3
occ_updated <- occ_updated[!, ]
plot(occ_updated,add=TRUE,col="red")  # Yes, they disappeared.

plot of chunk unnamed-chunk-12

3. Train models in R

3.1 Maxent

The following 3 conditions are important for running maxent in R.
(1) Install rJava package.
(2) I suggest use 32-bit R, because I assume there is an issue of rJava for 64-bit R.
(3) Also, we must download and copy the maxent.jar into a special folder, typically it is the java folder under dismo folder. In my case, it is: D:/Program Files/R/R-3.3.1/library/dismo/java/maxent.jar

There are two ways to run Maxent. One way is similar as the method when we run the interface of maxent, we need occurrence points and rasters; the other way is provide a data.frame with environmental conditions for presences and pseudo-absences.

Example of the 1st method. Different from the maxent interface, training model, projecting model, and evaluating model are typically independent steps.

# training model
myModel <- maxent(x=basemap,p=occ_updated)

# projecting model 
prediction_now <- predict (myModel, basemap)

# make future temperature warmer & wetter
futuremap <- basemap + 100
prediction_future <- predict (myModel, futuremap)

plot of chunk unnamed-chunk-13

More examples to be added: