Package for easy integration with R

Data included in the package ‘sdmpredictors’

The R package ‘sdmpredictors’ provides simple functions for querying and downloading the data layers.

  Installing the package


  Running the main functions

# Load package

# Explore datasets in the package

# Explore layers in a dataset

# Download specific layers to the current directory
bathy <- load_layers(c("BO_bathymin", "BO_bathymean", "BO_bathymax"))

# Check layer statistics

# Check Pearson correlation coefficient between layers

    * The package also contains external datasets (MARSPEC, BioClim)

  Sample code 1 : Maximum temperature at the sea bottom

# Load package

# Easy download of raster file (Maximum Temperature at the sea bottom)
temp.max.bottom <- load_layers("BO2_tempmax_bdmax")

# Crop raster to fit the North Atlantic
ne.atlantic.ext <- extent(-100, 45, 30.75, 72.5)
temp.max.bottom.crop <- crop(temp.max.bottom, ne.atlantic.ext)

# Generate a nice color ramp and plot the map
my.colors = colorRampPalette(c("#5E85B8","#EDF0C0","#C13127"))
plot(temp.max.bottom.crop,col=my.colors(1000),axes=FALSE, box=FALSE)
title(cex.sub = 1.25, sub = "Maximum temperature at the sea bottom (ºC)")

  Sample code 2 : Extracting environmental information for a set of sites

# Load packages (leaflet allows to load google maps)

# List layers avaialble in Bio-ORACLE v2
layers.bio2 <- list_layers( datasets="Bio-ORACLE" )

# Download environmental data layers (Max. Temperature, Min. Salinity and Min. Nitrates at the sea bottom)
environment.bottom <- load_layers( layercodes = c("BO2_tempmax_bdmean" , "BO2_salinitymin_bdmean", "BO2_nitratemin_bdmean") , equalarea=FALSE, rasterstack=TRUE)

# Download bathymetry
bathymetry <- load_layers("BO_bathymean")

# Generate a data.frame with the sites of interest
my.sites <- data.frame(Name=c("Faro, Portugal, NE Atlantic" , "Maspalomas, Spain, NE Atlantic" , "Guadeloupe, France, Caribbean Sea" , "Havana, Cuba, Caribbean Sea") , Lon=c(-7.873,-15.539,-61.208,-82.537) , Lat=c(37.047, 27.794,15.957,23.040 ) )

# Visualise sites of interest in google maps
m <- leaflet()
m <- addTiles(m)
m <- addMarkers(m, lng=my.sites$Lon, lat=my.sites$Lat, popup=my.sites$Name)

# Extract environmental values from layers
my.sites.environment <- data.frame(Name=my.sites$Name , depth=extract(bathymetry,my.sites[,2:3]) , extract(environment.bottom,my.sites[,2:3]) )

The data available in Bio-ORACLE are documented in two peer reviewed articles that you should cite:

Tyberghein L, Verbruggen H, Pauly K, Troupin C, Mineur F, De Clerck O (2012) Bio-ORACLE: A global environmental dataset for marine species distribution modelling. Global Ecology and Biogeography, 21, 272–281.
[access publication]   [supporting information]

Assis, J., Tyberghein, L., Bosh, S., Verbruggen, H., Serrão, E. A., & De Clerck, O. (2017). Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography.
[access publication]   [supporting information]

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