Graphical exploration of extrapolation or suitability pattern in the environmental and geographical space

Usage

p_extra(
  training_data,
  x = "x",
  y = "y",
  pr_ab = "pr_ab",
  extra_suit_data,
  projection_data,
  predictors = NULL,
  geo_space = TRUE,
  geo_position = "right",
  prop_points = 0.2,
  maxcells = 1e+05,
  alpha_p = 0.5,
  color_p = "black",
  alpha_gradient = 0.5,
  color_gradient = c("#FDE725", "#B3DC2B", "#6DCC57", "#36B677", "#1F9D87", "#25818E",
    "#30678D", "#3D4988", "#462777", "#440154"),
  theme = ggplot2::theme_classic()
)

Arguments

training_data: data.frame. Database with response (0,1) and predictor values used to fit a model.
x: character. Column name with spatial x coordinates
y: character. Column name with spatial y coordinates
pr_ab: character. Column name with species absence-presence, pseudo-absence-presence, or background-presence data (0,1).
extra_suit_data: SpatRaster. Raster layer with extrapolation or suitability values. extra_suit_data must have the same resolution and extent than projection_data
projection_data: SpatRaster. Raster layer with environmental variables used for model projection. projection_data must have the same resolution and extent than extra_suit_data
predictors: character. Vector of predictor name(s) to calculate partial dependence plots. If NULL all predictors will be used. Default NULL.
geo_space: logical. If TRUE will be produced a map. Default TRUE
geo_position: character. Map position regarding plot of environmental space, right, left, bottom, or upper. Default "right"
prop_points: numeric. Proportion of cells from extra_suit_data and projection_data to select for plotting. default. 0.5.
maxcells: integer. Maximum number of cells used to plot in the geographical space. Default 100000
alpha_p: numeric. a value between 0 to 1 to control transparency of presence-absence points. Lower values corresponding to more transparent colors. Default 0.5
color_p: character. A vector with a color used to color presence-absence points. Default "black"
alpha_gradient: numeric. a value between 0 to 1 to control transparency of projection data Lower values corresponding to more transparent colors. Default 0.5
color_gradient: character. A vector with colors used to color projection data. Default c( "#FDE725", "#B3DC2B", "#6DCC57", "#36B677", "#1F9D87", "#25818E", "#30678D", "#3D4988", "#462777", "#440154")
theme: ggplot2 theme. Default ggplot2::theme_classic()

Value

a plot

Examples

if (FALSE) {

require(dplyr)
require(terra)
require(ggplot2)

data(spp)
f <- system.file("external/somevar.tif", package = "flexsdm")
somevar <- terra::rast(f)
names(somevar) <- c("aet", "cwd", "tmx", "tmn")

spp$species %>% unique()
sp <- spp %>%
  dplyr::filter(species == "sp2", pr_ab == 1) %>%
  dplyr::select(x, y, pr_ab)

# Calibration area based on some criterion such as dispersal ability
ca <- calib_area(sp, x = "x", y = "y",
                 method = c("buffer", width = 50000), crs = crs(somevar))

plot(somevar[[1]])
points(sp)
plot(ca, add = T)


# Sampling pseudo-absences
set.seed(10)
psa <- sample_pseudoabs(
  data = sp,
  x = "x",
  y = "y",
  n = nrow(sp) * 2,
  method = "random",
  rlayer = somevar,
  calibarea = ca
)

# Merge presences and abasences databases to get a complete calibration data
sp_pa <- dplyr::bind_rows(sp, psa)
sp_pa

# Get environmental condition of calibration area
sp_pa_2 <- sdm_extract(data = sp_pa, x = "x", y = "y", env_layer = somevar)
sp_pa_2

# Measure extrapolation based on calibration data (presence and pseudo-absences)
# using SHAPE metric
extr <-
  extra_eval(
    training_data = sp_pa_2,
    pr_ab = "pr_ab",
    projection_data = somevar,
    metric = "mahalanobis",
    univar_comb = FALSE,
    n_cores = 1,
    aggreg_factor = 1
  )
plot(extr)

## %######################################################%##
####            Explore extrapolation in the            ####
####        environmental and geographical space        ####
## %######################################################%##

p_extra(
  training_data = sp_pa_2,
  x = "x",
  y = "y",
  pr_ab = "pr_ab",
  extra_suit_data = extr,
  projection_data = somevar,
  geo_space = TRUE,
  prop_points = 0.05
)

p_extra(
  training_data = sp_pa_2,
  x = "x",
  y = "y",
  pr_ab = "pr_ab",
  extra_suit_data = extr,
  projection_data = somevar,
  predictors = c("tmn", "cwd"),
  geo_space = TRUE,
  prop_points = 0.05
)

p_extra(
  training_data = sp_pa_2,
  x = "x",
  y = "y",
  pr_ab = "pr_ab",
  extra_suit_data = extr,
  projection_data = somevar,
  predictors = c("cwd", "tmx", "aet"),
  geo_space = TRUE,
  geo_position = "left",
  prop_points = 0.05,
  color_p = "white",
  alpha_p = 0.5,
  alpha_gradient = 0.2,
  color_gradient = c("#404096", "#529DB7", "#7DB874", "#E39C37", "#D92120"),
  theme = ggplot2::theme_dark()
)

p_extra(
  training_data = sp_pa_2,
  x = "x",
  y = "y",
  pr_ab = "pr_ab",
  extra_suit_data = extr,
  projection_data = somevar,
  geo_space = TRUE,
  prop_points = 0.05,
  color_p = "white",
  alpha_p = 0.5,
  alpha_gradient = 0.2,
  color_gradient = c("#404096", "#529DB7", "#7DB874", "#E39C37", "#D92120"),
  theme = ggplot2::theme_dark()
)

# Explore extrapolation only in the environmental space
p_extra(
  training_data = sp_pa_2,
  x = "x",
  y = "y",
  pr_ab = "pr_ab",
  extra_suit_data = extr,
  projection_data = somevar,
  geo_space = FALSE,
  prop_points = 0.05,
  color_p = "black",
  color_gradient = c("#085CF8", "#65AF1E", "#F3CC1D", "#FC6A9B", "#D70500"),
  theme = ggplot2::theme_minimal()
)


##%######################################################%##
####                 Explore univariate                 ####
####          and combinatorial extrapolation           ####
##%######################################################%##
extr <-
  extra_eval(
    training_data = sp_pa_2,
    pr_ab = "pr_ab",
    projection_data = somevar,
    metric = "mahalanobis",
    univar_comb = TRUE,
    n_cores = 1,
    aggreg_factor = 1
  )

plot(extr)


p_extra(
  training_data = sp_pa_2,
  x = "x",
  y = "y",
  pr_ab = "pr_ab",
  extra_suit_data = extr$uni_comb, # use  uni_comb layer
  projection_data = somevar,
  geo_space = TRUE,
  prop_points = 0.05,
  color_gradient = c("#B3DC2B","#25818E")
)

## %######################################################%##
####           With p_extra also is possible            ####
####       to explore the patterns of suitability       ####
## %######################################################%##

sp_pa_2 <- part_random(
  data = sp_pa_2,
  pr_ab = "pr_ab",
  method = c(method = "kfold", folds = 5)
)

rf_m1 <- fit_raf(
  data = sp_pa_2,
  response = "pr_ab",
  predictors = c("aet", "cwd", "tmx", "tmn"),
  partition = ".part",
  thr = c("max_sorensen")
)

suit <- sdm_predict(models = rf_m1, pred = somevar)
plot(suit$raf)
suit <- suit$raf

# Pasterns of suitability in geographical and environmental space
p_extra(
  training_data = sp_pa_2,
  x = "x",
  y = "y",
  pr_ab = "pr_ab",
  extra_suit_data = suit,
  projection_data = somevar,
  geo_space = TRUE,
  prop_points = 0.05,
)

# Pasterns of suitability plotting as points only presences
p_extra(
  training_data = sp_pa_2 %>%
    dplyr::filter(pr_ab == 1),
  x = "x",
  y = "y",
  pr_ab = "pr_ab",
  extra_suit_data = suit,
  projection_data = somevar,
  geo_space = TRUE,
  prop_points = 0.05,
)

# Pasterns of suitability in the environmental space only
# and plotting as points only presences
p_extra(
  training_data = sp_pa_2 %>%
    dplyr::filter(pr_ab == 1),
  x = "x",
  y = "y",
  pr_ab = "pr_ab",
  extra_suit_data = suit,
  projection_data = somevar,
  geo_space = FALSE,
  prop_points = 0.05,
)
}