naturaList (version 0.5.2)

clean_eval: Evaluate the cleaning of occurrences records

Description

This function compare the area occupied by a species before and after pass through the cleaning procedure according to the chosen level of filter. The comparison can be made by measuring area in the geographical and in the environmental space

Usage

clean_eval(
  occ.cl,
  geo.space,
  env.space = NULL,
  level.filter = c("1_det_by_spec"),
  r,
  species = "species",
  decimal.longitude = "decimalLongitude",
  decimal.latitude = "decimalLatitude",
  scientific.name,
  longitude,
  latitude
)

Value

a list in which:

area data frame remaining area after cleaning proportional to the area before cleaning. The values vary from 0 to 1. Column named r.geo.area

is the remaining area for all species in the geographic space and the

r.env.area in the environmental space.

comp data frame with composition of species in sites (cells from raster layers) before cleaning (comp$comp$BC) and after cleaning (comp$comp$AC). The number of rows is equal the number of cells in

r, and number of columns is equal to the number of species in the

occ.cl.

rich data frame with a single column with the richness of each site

site.coords data frame with site's coordinates. It facilitates to built raster layers from results using rasterFromXYZ

Arguments

occ.cl

data frame with occurrence records information already classified by classify_occ function.

geo.space

a SpatialPolygons* or sf object defining the geographical space

env.space

a SpatialPolygons* or sf object defining the environmental space. Use the define_env_space for create this object. By default env.space = NULL, hence do not evaluate the cleaning in the environmental space.

level.filter

a character vector including the levels in 'naturaList_levels' column which filter the occurrence data set.

r

a raster with 2 layers representing the environmental variables. If env.space = NULL, it could be a single layer raster, from which the cell size and extent are extracted to produce the composition matrix.

species

column name of occ.cl with the species names.

decimal.longitude

column name of occ.cl longitude in decimal degrees.

decimal.latitude

column name of occ.cl latitude in decimal degrees.

scientific.name

deprecated, use species instead.

longitude

deprecated, use decimal.longitude instead

latitude

deprecated, use decimal.latitude instead

See Also

define_env_space

Examples

if (FALSE) {

library(sp)
library(raster)


data("speciaLists") # list of specialists
data("cyathea.br") # occurrence dataset


# classify
occ.cl <- classify_occ(cyathea.br, speciaLists)

# delimit the geographic space
# land area
data("BR")


# Transform occurrence data in SpatialPointsDataFrame
spdf.occ.cl <- sp::SpatialPoints(occ.cl[, c("decimalLongitude", "decimalLatitude")])


# load climate data
data("r.temp.prec") # mean temperature and annual precipitation
df.temp.prec <- raster::as.data.frame(r.temp.prec)

### Define the environmental space for analysis
# this function will create a boundary of available environmental space,
# analogous to the continent boundary in the geographical space
env.space <- define_env_space(df.temp.prec, buffer.size = 0.05)

# filter by year to be consistent with the environmental data
occ.class.1970 <-  occ.cl %>%
  dplyr::filter(year >= 1970)

### run the evaluation
cl.eval <- clean_eval(occ.class.1970,
                      env.space = env.space,
                      geo.space = BR,
                      r = r.temp.prec)

#area results
head(cl.eval$area)


### richness maps
## it makes sense if there are more than one species
rich.before.clean <- raster::rasterFromXYZ(cbind(cl.eval$site.coords,
                                                 cl.eval$rich$rich.BC))
rich.after.clean <- raster::rasterFromXYZ(cbind(cl.eval$site.coords,
                                                cl.eval$rich$rich.AC))

raster::plot(rich.before.clean)
raster::plot(rich.after.clean)

### species area map
comp.bc <- as.data.frame(cl.eval$comp$comp.BC)
comp.ac <- as.data.frame(cl.eval$comp$comp.AC)

c.villosa.bc <- raster::rasterFromXYZ(cbind(cl.eval$site.coords,
                                            comp.bc$`Cyathea villosa`))
c.villosa.ac <- raster::rasterFromXYZ(cbind(cl.eval$site.coords,
                                            comp.ac$`Cyathea villosa`))

raster::plot(c.villosa.bc)
raster::plot(c.villosa.ac)
}