sdmtools

A set of helper functions to facilitate species distribution modelling.

Installation

You can install sdmtools with:

install.packages(
"sdmtools",
repos = "https://idem-lab.r-universe.dev"
)

Data

library(sdmtools)

raster_to_terra — an annotated equivalence table of functions from the raster and terra. First 5 lines:

raster	terra	comment for terra use
raster, brick, stack	rast	NA
rasterFromXYZ	rast(, type=‘xyz’)	note arg `type = xyz`
stack, addLayer	c	NA
addLayer	add\<-	NA
area	cellSize or expanse	NA

global_regions — a tibble showing the WHO region, UN region, and continent for for 249 countries and country-like things. First 5 lines:

country	iso2	iso3	who_region	un_region	continent
Afghanistan	AF	AFG	Eastern Mediterranean	Asia-Pacific States	Asia
Albania	AL	ALB	Europe	Eastern European States	Europe
Algeria	DZ	DZA	Africa	African states	Africa
American Samoa	AS	ASM	NA	NA	Oceania
Andorra	AD	AND	Europe	Western European and other States	Europe

Data-generating functions

The package terra is fiddly about storing its spat... objects in packages, so we chose to generate example spatial data on-demand using functions, rather than storing it.

example_raster — an example spatRaster.

library(terra)
#> terra 1.7.79

r <- example_raster()
r
#> class       : SpatRaster 
#> dimensions  : 10, 10, 1  (nrow, ncol, nlyr)
#> resolution  : 1, 1  (x, y)
#> extent      : 0, 10, 0, 10  (xmin, xmax, ymin, ymax)
#> coord. ref. :  
#> source(s)   : memory
#> name        :   example 
#> min value   : 0.0627102 
#> max value   : 7.3352526

plot(r)

example_vector — an example spatVector.

library(terra)
v <- example_vector()
v
#>  class       : SpatVector 
#>  geometry    : points 
#>  dimensions  : 10, 0  (geometries, attributes)
#>  extent      : 0.2293562, 8.00672, 1.375653, 8.951683  (xmin, xmax, ymin, ymax)
#>  coord. ref. :

plot(v)

make_africa_mask — makes a mask layer of Africa based on shapefiles from malariaAtlas::getShp. Can produce either a SpatRaster or SpatVector.

library(terra)
africa_mask <- make_africa_mask(type = "vector")
#> Loading ISO 19139 XML schemas...
#> Loading ISO 19115 codelists...
#> Please Note: Because you did not provide a version, by default the version being used is 202403 (This is the most recent version of admin unit shape data. To see other version options use function listShpVersions)
#> although coordinates are longitude/latitude, st_union assumes that they are
#> planar
#> Warning: [crs<-] not all geometries were transferred, use svc for a geometry
#> collection

plot(africa_mask)

Function examples

rastpointplot — simple utility to plot a raster with points over it.

rastpointplot(r,v)

source_R — source all R files in a target directory

source_R("/Users/frankenstein/project/R") # do not run

import_rasts — import all rasters from a directory into a single object

rasters <- import_rasts("/data/grids/covariates") # do not run

split_rast — split a raster.

r <- example_raster()

s <- split_rast(r, grain = 2)

s
#> [[1]]
#> class       : SpatRaster 
#> dimensions  : 5, 5, 1  (nrow, ncol, nlyr)
#> resolution  : 1, 1  (x, y)
#> extent      : 0, 5, 0, 5  (xmin, xmax, ymin, ymax)
#> coord. ref. :  
#> source(s)   : memory
#> name        :   example 
#> min value   : 0.1587361 
#> max value   : 7.3352526 
#> 
#> [[2]]
#> class       : SpatRaster 
#> dimensions  : 5, 5, 1  (nrow, ncol, nlyr)
#> resolution  : 1, 1  (x, y)
#> extent      : 0, 5, 5, 10  (xmin, xmax, ymin, ymax)
#> coord. ref. :  
#> source(s)   : memory
#> name        :   example 
#> min value   : 0.1028045 
#> max value   : 4.0001839 
#> 
#> [[3]]
#> class       : SpatRaster 
#> dimensions  : 5, 5, 1  (nrow, ncol, nlyr)
#> resolution  : 1, 1  (x, y)
#> extent      : 5, 10, 0, 5  (xmin, xmax, ymin, ymax)
#> coord. ref. :  
#> source(s)   : memory
#> name        :    example 
#> min value   : 0.09802478 
#> max value   : 3.23820739 
#> 
#> [[4]]
#> class       : SpatRaster 
#> dimensions  : 5, 5, 1  (nrow, ncol, nlyr)
#> resolution  : 1, 1  (x, y)
#> extent      : 5, 10, 5, 10  (xmin, xmax, ymin, ymax)
#> coord. ref. :  
#> source(s)   : memory
#> name        :   example 
#> min value   : 0.0627102 
#> max value   : 5.7145289

ps <- lapply(
  s,
  FUN = extend,
  y = r
) |>
  rast()

c(
  r,
  ps
) |>
  plot()

Functions for a species distribution modelling workflow

We have some covariate layers: cov1 and cov2

library(terra)

cov1 <- example_raster(
  seed = -44,
  layername = "cov1"
)
cov2 <- example_raster(
  seed = 15.3,
  layername = "cov2"
)

covs <- c(cov1, cov2)

std_rast — standardise a spatRaster by transforming it to have a range of 0—1

cov1_st <- std_rast(cov1)

plot(cov1_st)

We have some presences and absences

presences <- example_vector(seed = 68) %>%
  as.data.frame(geom = "xy")
absences <- example_vector(seed = 9.6) %>%
  as.data.frame(geom = "xy")

presences
#>           x        y
#> 1  9.244899 5.033042
#> 2  6.612025 1.559797
#> 3  4.024099 8.750261
#> 4  6.370063 4.438317
#> 5  3.526324 6.598762
#> 6  7.476441 7.754586
#> 7  7.175489 8.123659
#> 8  1.935898 5.082858
#> 9  3.331217 7.974853
#> 10 1.365547 5.741829

extract_covariates — extract covariate values from spatRaster or raster layers for a given set of points

Pass in either presences and absences as a data.frame or tibble of with , or presences_and_absences as a single data frame points with a presence or ID column(s)

sdm_data <- extract_covariates(
  covariates = covs,
  presences = presences,
  absences = absences
)

We can then make a spatial prediction of our model using predict_sdm and write and read it out in a single step with writereadrast, and write it to a temporary file with temptif:

# first we make a simple model, using data from above
m <- glm(presence ~ cov1 + cov2, data = sdm_data)

prediction_rast <- predict_sdm(m, covs) |>
  writereadrast(filename = temptif())

plot(prediction_rast)