EOO.computing: Extent of Occurrences multi-taxa computation

In gdauby/ConR: Computation of Parameters Used in Preliminary Assessment of Species Conservation Status

Extent of Occurrences multi-taxa computation

Description

Compute extent of occurrences (EOO) for multiple taxa in square kilometers and provide polygons used for EOO computation

Usage

EOO.computing(
  XY,
  exclude.area = FALSE,
  country_map = NULL,
  export_shp = FALSE,
  driver_shp = "ESRI Shapefile",
  write_shp = FALSE,
  alpha = 1,
  buff.alpha = 0.1,
  method.range = "convex.hull",
  method.less.than3 = "not comp",
  file.name = "EOO.results",
  parallel = FALSE,
  NbeCores = 2,
  show_progress = TRUE,
  proj_type = "cea",
  mode = "spheroid"
)

Arguments

XY	dataframe see Details
exclude.area	a logical, if TRUE, areas outside of country_map are cropped of SpatialPolygons used for calculating EOO. By default is FALSE
country_map	a SpatialPolygonsDataFrame or SpatialPolygons showing for example countries or continent borders. This shapefile will be used for cropping the SpatialPolygonsl if exclude.area is TRUE
export_shp	a logical, whether shapefiles should be exported or not, see Value. By default is FALSE
driver_shp	a string, define the driver for exporting shapefiles, by default "ESRI Shapefile". See sf::st_write()
write_shp	a logical, if TRUE, export SpatialPolygons used for EOO computation as ESRI shapefiles in the working directory. By default is FALSE
alpha	a numeric, if method.range is "alpha.hull", value of alpha of the alpha hull, see alphahull::ahull(). By default is 1
buff.alpha	a numeric, if method.range is "alpha.hull", define the buffer in decimal degree added to alpha hull. By default is 0.1
method.range	a character string, "convex.hull" or "alpha.hull". By default is "convex.hull"
method.less.than3	a character string. If equal to "arbitrary", will give a value to species with two unique occurrences, see Details. By default is "not comp"
file.name	a character string. Name file for exported results in csv file. By default is "EOO.results"
parallel	a logical. Whether running should be performed in parallel. FALSE by default.
NbeCores	an integer. Register the number of cores for parallel execution. Two by default.
show_progress	logical. Whether progress informations should displayed. TRUE by default
proj_type	string or numeric
mode	character string either 'spheroid' or 'planar'. By default 'spheroid'

Details

Input as a dataframe should have the following structure:

It is mandatory to respect field positions, but field names do not matter

1	ddlat	numeric, latitude (in decimal degrees)
2	ddlon	numeric, longitude (in decimal degrees)
3	tax	character or factor, taxa names

If exclude.area is TRUE and country_map is not provided, the world country polygons used comes from the package rnaturalearth

By default (mode = "spheroid"), the area of the polygon is based on a polygon in longitude/latitude coordinates considering the earth as an ellipsoid.

To make a polygon more accurate, the function use the function st_segmentize from the sf package. This adds vertices on the great circles (in order to make shortest distances between points, see example below) which can make difference for species with large distribution.

An estimation of EOO based on projected data is also possible (mode = "planar"). This allow the user to use its own projection. By default, the projection (proj_type = "cea") is a global cylindrical equal-area projection. It can makes sense to use a planar projection to estimate the EOO. See example.

Important notes:

EOO will only be computed if there is at least three unique occurrences unless method.less.than3 is put to "arbitrary". In that specific case, EOO for species with two unique occurrences will be equal to DistDist0.1 where Dist is the distance in kilometers separating the two points.

For the very specific (and infrequent) case where all occurrences are localized on a straight line (in which case EOO would be null), 'noises' are added to coordinates, using the function jitter. There is a warning when this happens. This means that EOO value will not be constant across multiple estimation (although the variation should be small)

Limitation

For a species whose occurrences span more than 180 degrees, EOO should not be considered. This is the case for example for species whose distribution span the 180th meridian.

Value

If export_shp is FALSE, a dataframe with one field containing EOO in square kilometers. NA is given when EOO could not be computed because there is less than three unique occurrences (or two if method.less.than3 is put to "arbitrary").

If export_shp is TRUE, a list with:

1. results a data.frame

2. spatial used for EOO computation

Author(s)

Gilles Dauby

[e-mail]

References

Gaston & Fuller 2009 The sizes of species' geographic ranges, Journal of Applied Ecology, 49 1-9

See Also

alphahull::ahull()

https://github.com/azizka/speciesgeocodeR

Examples

set.seed(2)
dataset <- dummy_dist(nsp = 3, max_occ = 30)
EOO.computing(XY = dataset, export_shp = TRUE)

EOO.computing(XY = dataset, mode = "planar")

EOO.computing(XY = dataset, method.range = "alpha")

res <- EOO.computing(XY = dataset, export_shp = TRUE)
res$spatial



country_map <-
  rnaturalearth::ne_countries(scale = 50, returnclass = "sf", type = "map_units")

### example Large distribution
# Spheroid estimation
par(mfrow = c(2,2))
XY <- 
  data.frame(x = c(-20, 5, 0, -20, -20), 
             y = c(-65, -45, -40, -55, -40),
             taxa = "species")
p1 <- 
  EOO.computing(XY =  XY, export_shp = TRUE)
p1$results

plot(st_geometry(country_map), extent = p1$spatial)
plot(p1$spatial, lwd = 2, col = "red",  add= TRUE)

p2 <- 
  EOO.computing(XY = XY, mode = "planar", export_shp = TRUE)
p2$results
plot(st_geometry(country_map), extent = p2$spatial)
plot(p2$spatial, lwd = 2, col = "red",  add= TRUE)

# World Mercartor projection
p2 <- 
  EOO.computing(XY = XY, mode = "planar", proj_type = 3395, export_shp = TRUE)
p2$results
plot(st_geometry(country_map), extent = p2$spatial)
plot(p2$spatial, lwd = 2, col = "red",  add= TRUE)


### example Antartic distribution
XY <- 
  data.frame(x = c(-65, -62, -78, -65), 
             y = c(-150, 0, 120, 150),
             taxa = "species")

## This throws an error
# p1 <- 
#   EOO.computing(XY = XY, mode = "planar", export_shp = TRUE) 

p1 <- 
  EOO.computing(XY = XY, mode = "planar", proj_type = "Antarctic", export_shp = TRUE)


p1 <- st_transform(p1$spatial, proj_crs(proj_type = "Antarctic"))
plot(st_geometry(st_transform(country_map, proj_crs(proj_type = "Antarctic"))), 
     extent = p1)
plot(p1, lwd = 2, col = 'red', add = TRUE)


## Parallel computation becomes advantageous for very large dataset
# library(microbenchmark)
# microbenchmark(EOO.computing(XY = dummy_dist(nsp = 1000, seed = 1)), 
# EOO.computing(XY = dummy_dist(nsp = 1000, seed = 1), parallel = T, NbeCores = 4), times = 2)

gdauby/ConR documentation built on Jan. 30, 2024, 11:10 p.m.