Evaluate the cleaning of occurrences records

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

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
)

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

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

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)
}

Arguments

Value

See also

Examples