Skip to contents

Delimit calibration area for constructing species distribution models

Source: R/calib_area.R
calib_area.Rd

This function offers different methods to define the calibration area. The output could be used with other flexsdm functions like sample_backgroud, sample_pseudoabs, and sdm_predict, among others

Usage

calib_area(data, x, y, method, groups = NULL, crs = NULL)

Arguments

data

data.frame or tibble. Database with presences

x

character. Column name with longitude data

y

character. Column name with latitude data

method

character. Method used for delimiting a calibration area. Could be necessary to concatenate (c()) different objects for this argument. The following methods are implemented:

  • buffer: calibration area is defined by a buffer around presences. Usage method = c('buffer', width=40000). A value of the buffer width in m must be provided if CRS has a longitude/latitude, or in map units in other cases

  • mcp: calibration area is defined by a minimum convex polygon. Usage method = 'mcp'.

  • bmcp: calibration area is defined by buffered minimum convex polygon with buffer width. Usage method = c('bmcp', width=40000). A value of the buffer width in m must be provided if CRS has a longitude/latitude, or in map units in other cases

  • mask: calibration area is defined by selected polygons in a spatial vector object intersected by presences. Usage method = c("mask", clusters, "DN"). The second concatenated element must be a SpatVector, the third element is a character with the column name from SpatVector used for filtering polygons.

groups

character. Column name indicating differentiated subsets of points. This could be used with mcp and bmcp method. Default NULL

crs

character. Coordinate reference system used for transforming occurrences and outputs. If set as NULL, the result of mask method will have the same crs as the SpatVector used. Define a crs is mandatory for buffer, mcp and bmcp method.

Value

A SpatVector

Examples

# \donttest{
require(terra)
#> Loading required package: terra
#> Warning: package 'terra' was built under R version 4.5.3
#> terra 1.9.50
#> 
#> Attaching package: 'terra'
#> The following objects are masked from 'package:testthat':
#> 
#>     compare, describe
require(dplyr)
data("spp")
clusters <- system.file("external/clusters.shp", package = "flexsdm")
clusters <- terra::vect(clusters)


single_spp <-
  spp %>%
  dplyr::filter(species == "sp1") %>%
  dplyr::filter(pr_ab == 1) %>%
  dplyr::select(-pr_ab)


plot(clusters)
points(single_spp[-1], col = "red")

crs(clusters, proj = TRUE) # coordinate reference system (CRS) used for this points database
#> [1] "+proj=aea +lat_0=0 +lon_0=-120 +lat_1=34 +lat_2=40.5 +x_0=0 +y_0=-4000000 +datum=NAD83 +units=m +no_defs"
# note that the unit of this CRS is in m, consequently the buffer width
# will be interpreted in m too

# buffer method
ca_1 <- calib_area(
  data = single_spp,
  x = "x",
  y = "y",
  method = c("buffer", width = 40000),
  crs = crs(clusters)
)
plot(ca_1)
points(single_spp[, 2:3], pch = 19, cex = 0.5)


# mcp method
ca_2 <- calib_area(
  data = single_spp,
  x = "x",
  y = "y",
  method = "mcp",
  crs = crs(clusters)
)
plot(ca_2)
points(single_spp[, 2:3], pch = 19, cex = 0.5)


# mcp method for different groups
single_spp <- single_spp %>% mutate(groups = ifelse(x > 150000, "a", "b"))

plot(single_spp[, 2:3], pch = 19, col = "blue")
points(single_spp[single_spp$groups == "a", 2:3], col = "red", pch = 19)
points(single_spp[, 2:3])


ca_2.1 <- calib_area(
  data = single_spp,
  x = "x",
  y = "y",
  method = c("mcp"),
  crs = crs(clusters),
  groups = "groups"
)
plot(ca_2.1)
points(single_spp[, 2:3], pch = 19, cex = 0.5)


# bmcp method
ca_3 <- calib_area(
  data = single_spp,
  x = "x",
  y = "y",
  method = c("bmcp", width = 30000),
  crs = crs(clusters)
)
plot(ca_3)
points(single_spp[, 2:3], pch = 19, cex = 0.5)


# bmcp method for different groups
ca_3.1 <- calib_area(
  data = single_spp,
  x = "x",
  y = "y",
  method = c("bmcp", width = 30000),
  crs = crs(clusters),
  groups = "groups"
)
plot(ca_3.1)
points(single_spp[, 2:3], pch = 19, cex = 0.5)


# mask method
plot(clusters)

names(clusters)
#> [1] "clusters"

ca_3.1 <- calib_area(
  data = single_spp,
  x = "x",
  y = "y",
  method = c("mask", clusters, "clusters"),
)
plot(ca_3.1)
points(single_spp[, 2:3], pch = 19, cex = 0.5, col = "red")

# }