## ----setup, include = TRUE----------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")

ext_file <- function(...) {
  path <- system.file("extdata", ..., package = "gbif.range")
  if (nzchar(path)) {
    return(path)
  }
  normalizePath(file.path("..", "inst", "extdata", ...), mustWork = TRUE)
}

library(gbif.range)

## ----data-retrieval, eval = FALSE---------------------------------------------
# # Study extent, world boundaries, and packaged terrestrial ecoregions
# shp.lonlat <- terra::vect(ext_file("shp_lonlat.shp"))
# countries <- terra::vect(ext_file("world_countries.shp"))
# eco.terra <- read_ecoreg(
#   ecoreg_name = "eco_terra", save_dir = tempdir()
# )
# 
# # Custom, high-resolution ecoregions for the Alps study extent
# # ('rst' --> 5x5-km resolution)
# rst <- terra::rast(ext_file("rst.tif"))
# my.eco <- make_ecoreg(env = rst, nclass = 200, format = "SpatVector")
# 
# # Continent extent to keep only terrestrial my.eco
# contExtL.shp <- terra::aggregate(
#   terra::crop(countries, terra::ext(rst))
# )
# contExtS.shp <- terra::crop(
#   contExtL.shp,
#   terra::ext(shp.lonlat)
# )
# my.ecoS <- intersect(my.eco, contExtS.shp)
# 
# # GBIF occurrences for the two focal species used throughout
# obs.arcto <- get_gbif(
#   sp_name = "Arctostaphylos alpinus",
#   geo = shp.lonlat,
#   grain = 1
# )
# obs.pt <- get_gbif(sp_name = "Panthera tigris")
# 
# # Diversity rasters used in Figure 3
# iucn.robin <- terra::rast(ext_file("iucn_div_robin.tif"))
# gf.robin <- terra::rast(ext_file("gf_div_robin.tif"))

## ----fig1a-left, eval = FALSE-------------------------------------------------
# # Regional range at ~5x5-km resolution ('my.eco' resolution)
# range.arcto <- get_range(
#   occ_coord = obs.arcto,
#   ecoreg = my.eco,
#   ecoreg_name = "EcoRegion",
#   format = "SpatVector"
# )
# 
# # Assign colors to ecoregions
# range.arctoS <- terra::crop(range.arcto$rangeOutput, contExtS.shp)
# col.palette <- grDevices::colorRampPalette(c(
#   "#a6cee3", "#1f78b4", "#b2df8a", "#33a02c",
#   "#fb9a99", "#e31a1c", "#fdbf6f", "#ff7f00",
#   "#cab2d6", "#6a3d9a", "#ffff99", "#b15928"
# ))
# colcol <- col.palette(length(my.ecoS))
# set.seed(7)
# my.ecoS$color <- sample(
#   paste0(colcol, ""),
#   length(my.ecoS),
#   replace = FALSE
# )
# 
# pt.col <- terra::extract(
#   x = my.ecoS,
#   y = as.data.frame(
#     obs.arcto[, c(
#       "decimalLongitude", "decimalLatitude"
#     )]
#   )
# )
# pt.plot <- obs.arcto[
#   !is.na(pt.col$color),
#   c("decimalLongitude", "decimalLatitude")
# ]
# pt.col2 <- pt.col[!is.na(pt.col$color), "color"]
# pt.col3 <- grDevices::adjustcolor(
#   pt.col2, red.f = 0.6, green.f = 0.6,
#   blue.f = 0.6
# )
# 
# # Plot
# terra::plot(
#   my.ecoS,
#   col = paste0(my.ecoS$color, "99"),
#   border = NA,
#   axes = FALSE
# )
# terra::plot(
#   merge_range(range.arctoS),
#   border = "black",
#   lwd = 1,
#   col = "#00000099",
#   add = TRUE
# )
# graphics::points(
#   pt.plot, col = pt.col2, pch = 16, cex = 1
# )
# graphics::points(
#   pt.plot, col = pt.col3, pch = 16, cex = 0.6
# )

## ----fig-1a-left, echo = FALSE, out.width = "50%"-----------------------------
knitr::include_graphics("../man/figures/Fig1a_left.png")

## ----fig1a-right, eval = FALSE------------------------------------------------
# # Global range
# range.tiger <- get_range(
#   occ_coord = obs.pt,
#   ecoreg = eco.terra,
#   ecoreg_name = "ECO_NAME",
#   format = "SpatVector"
# )
# 
# # Assign colors to ecoregions
# ext.tiger.eco <- terra::ext(range.tiger$rangeOutput)
# ext.tiger.eco  <- c(
#   ext.tiger.eco[1] - 2, ext.tiger.eco[2] + 2,
#   ext.tiger.eco[3] - 2, ext.tiger.eco[4] + 2
# )
# eco.local <- terra::crop(eco.terra, ext.tiger.eco)
# col.palette <- grDevices::colorRampPalette(c(
#   "#a6cee3", "#1f78b4", "#b2df8a", "#33a02c",
#   "#fb9a99", "#e31a1c", "#fdbf6f", "#ff7f00",
#   "#cab2d6", "#6a3d9a", "#ffff99", "#b15928"
# ))
# colcol <- col.palette(length(eco.local))
# set.seed(3)
# eco.local$color <- sample(
#   paste0(colcol, ""),
#   length(eco.local),
#   replace = FALSE
# )
# pt.coords <- as.data.frame(
#   obs.pt[, c("decimalLongitude", "decimalLatitude")]
# )
# pt.col <- terra::extract(eco.local, pt.coords)
# pt.plot <- obs.pt[
#   !is.na(pt.col$color),
#   c("decimalLongitude", "decimalLatitude")
# ]
# pt.col2 <- pt.col[!is.na(pt.col$color), "color"]
# pt.col3 <- grDevices::adjustcolor(
#   pt.col2, red.f = 0.6, green.f = 0.6,
#   blue.f = 0.6
# )
# out.plot <- terra::extract(range.tiger$rangeOutput, pt.coords)
# op.na <- is.na(out.plot[, 2])
# out.plot <- obs.pt[
#   op.na, c("decimalLongitude", "decimalLatitude")
# ]
# 
# # Plot
# terra::plot(
#   eco.local,
#   col = eco.local$color,
#   border = NA,
#   axes = FALSE
# )
# terra::plot(
#   merge_range(range.tiger),
#   border = "black",
#   lwd = 2,
#   col = "#63636399",
#   add = TRUE
# )
# graphics::points(
#   pt.plot, col = pt.col2, pch = 16, cex = 1.5
# )
# graphics::points(
#   pt.plot, col = pt.col3, pch = 16, cex = 0.8
# )
# graphics::points(
#   out.plot, col = "black", pch = 4, cex = 1.5,
#   lwd = 2
# )

## ----fig-1a-right, echo = FALSE, out.width = "50%"----------------------------
knitr::include_graphics("../man/figures/Fig1a_right.png")

## ----fig2a, eval = FALSE------------------------------------------------------
# # ------------------------------------------
# # Arctostaphylos alpinus (custom ecoregion)
# # ------------------------------------------
# 
# # Create pseudo-absences
#   # First remove observations considered outliers in get_range()
# xy.df <- range.arcto$init.args$occ_coord
# r.ext <- terra::ext(range.arcto$rangeOutput)
# Xrm.cond <- xy.df$decimalLongitude >= r.ext[1] &
#   xy.df$decimalLongitude <= r.ext[2]
# Yrm.cond <- xy.df$decimalLatitude >= r.ext[3] &
#   xy.df$decimalLatitude <= r.ext[4]
# xy.df <- xy.df[Xrm.cond & Yrm.cond, ]
#   # Sample n regular background points over the range extent
# x.interv <- (r.ext[2] - r.ext[1]) / (sqrt(1e4) - 1)
# y.interv <- (r.ext[4] - r.ext[3]) / (sqrt(1e4) - 1)
# lx <- seq(r.ext[1], r.ext[2], x.interv)
# ly <- seq(r.ext[3], r.ext[4], y.interv)
# bp.xy <- expand.grid(
#   decimalLongitude = lx,
#   decimalLatitude = ly
# )
# 
# # Combine observations with background
# obs.xy <- xy.df[, c("decimalLongitude", "decimalLatitude")]
# all.xy <- rbind(obs.xy, bp.xy)
# all.xy$Pres <- 0
# all.xy[1:nrow(obs.xy), "Pres"] <- 1
# 
# # Run block-cv
# xy.pres <- all.xy$Pres
# cv.strat <- make_blocks(
#   nfolds = 5,
#   df = all.xy[, c("decimalLongitude", "decimalLatitude")],
#   nblocks = 5 * 2,
#   pres = xy.pres
# )
# all.xy$bcv <- cv.strat
# all.xy[all.xy$bcv %in% 1, "col"] <- "#e41a1c"
# all.xy[all.xy$bcv %in% 2, "col"] <- "#377eb8"
# all.xy[all.xy$bcv %in% 3, "col"] <- "#4daf4a"
# all.xy[all.xy$bcv %in% 4, "col"] <- "#984ea3"
# all.xy[all.xy$bcv %in% 5, "col"] <- "#ff7f00"
# all.xy[all.xy$Pres %in% 1, "col"] <- substr(
#   grDevices::adjustcolor(
#     all.xy[all.xy$Pres %in% 1, "col"],
#     red.f = 0.5, green.f = 0.5, blue.f = 0.5
#   ),
#   1, 7
# )
# 
# # Evaluate
# ar.test <- cv_range(
#   range_object = range.arcto,
#   cv = "block-cv",
#   nfolds = 5,
#   nblocks = 2
# )
# 
# # Use the broader (L) extent and country borders so
# # the evaluation text fits on the plot
# ext.L <- terra::ext(contExtL.shp)
# world.local.ar <- terra::crop(countries, ext.L)
# world.local.ar <- terra::aggregate(world.local.ar)
# pres <- all.xy[all.xy$Pres %in% 1, ]
# abs.pts <- all.xy[all.xy$Pres %in% 0, ]
# pres_coords <- as.data.frame(
#   pres[, c("decimalLongitude", "decimalLatitude")]
# )
# id.in <- terra::extract(
#   range.arcto$rangeOutput, pres_coords
# )
# pres <- pres[!is.na(id.in[, 2]), ]
# 
# # Plot
# terra::plot(
#   world.local.ar, col = "#bcd1bc",
#   axes = FALSE, lwd = 1
# )
# graphics::points(
#   abs.pts, col = paste0(abs.pts$col, "50"),
#   pch = 16, cex = 0.5
# )
# terra::plot(
#   merge_range(range.arctoS),
#   border = "black", lwd = 1.7,
#   col = "#63636370", add = TRUE
# )
# graphics::points(
#   pres, col = paste0(pres$col, "90"),
#   pch = 16, cex = 1.3
# )
# 
# # Evaluation text anchored to the L extent's bottom-left corner
# txt.x <- ext.L[1] + 0.02 * (ext.L[2] - ext.L[1])
# txt.y1 <- ext.L[3] + 0.09 * (ext.L[4] - ext.L[3])
# txt.y2 <- ext.L[3] + 0.04 * (ext.L[4] - ext.L[3])
# 
# graphics::text(
#   txt.x, txt.y1,
#   paste(
#     "Mean TSS =",
#     round(tail(ar.test[, "TSS"], 1), 2)
#   ),
#   cex = 1.2, font = 2, adj = 0
# )
# graphics::text(
#   txt.x, txt.y2,
#   paste(
#     "Mean Precision =",
#     round(tail(ar.test[, "Precision"], 1), 2)
#   ),
#   cex = 1.2, font = 2, adj = 0
# )

## ----fig-2a, echo = FALSE, out.width = "50%"----------------------------------
knitr::include_graphics("../man/figures/Fig2a_arcto_cv.png")

## ----fig2b, eval = FALSE------------------------------------------------------
# # ------------------------------------------
# # Panthera tigris (packaged ecoregion)
# # ------------------------------------------
# 
# ext.temp.t2 <- terra::ext(range.tiger$rangeOutput)
# ext.temp.t2  <- c(
#   ext.temp.t2[1] - 0.2, ext.temp.t2[2] + 0.2,
#   ext.temp.t2[3] - 0.2, ext.temp.t2[4] + 0.2
# )
# 
# # Create pseudo-absences
#   # First remove observations considered outliers in get_range()
# xy.df <- range.tiger$init.args$occ_coord
# r.ext <- terra::ext(range.tiger$rangeOutput)
# Xrm.cond <- xy.df$decimalLongitude >= r.ext[1] &
#   xy.df$decimalLongitude <= r.ext[2]
# Yrm.cond <- xy.df$decimalLatitude >= r.ext[3] &
#   xy.df$decimalLatitude <= r.ext[4]
# xy.df <- xy.df[Xrm.cond & Yrm.cond, ]
#   # Sample n regular background points over the range extent
# x.interv <- (r.ext[2] - r.ext[1]) / (sqrt(1e4) - 1)
# y.interv <- (r.ext[4] - r.ext[3]) / (sqrt(1e4) - 1)
# lx <- seq(r.ext[1], r.ext[2], x.interv)
# ly <- seq(r.ext[3], r.ext[4], y.interv)
# bp.xy <- expand.grid(
#   decimalLongitude = lx,
#   decimalLatitude = ly
# )
# 
# # Combine observations with background
# obs.xy <- xy.df[, c("decimalLongitude", "decimalLatitude")]
# all.xy <- rbind(obs.xy, bp.xy)
# all.xy$Pres <- 0
# all.xy[1:nrow(obs.xy), "Pres"] <- 1
# 
# # Run block-cv
# xy.pres <- all.xy$Pres
# cv.strat <- make_blocks(
#   nfolds = 5,
#   df = all.xy[, c("decimalLongitude", "decimalLatitude")],
#   nblocks = 5 * 2,
#   pres = xy.pres
# )
# all.xy$bcv <- cv.strat
# all.xy[all.xy$bcv %in% 1, "col"] <- "#e41a1c"
# all.xy[all.xy$bcv %in% 2, "col"] <- "#377eb8"
# all.xy[all.xy$bcv %in% 3, "col"] <- "#4daf4a"
# all.xy[all.xy$bcv %in% 4, "col"] <- "#984ea3"
# all.xy[all.xy$bcv %in% 5, "col"] <- "#ff7f00"
# all.xy[all.xy$Pres %in% 1, "col"] <- substr(
#   grDevices::adjustcolor(
#     all.xy[all.xy$Pres %in% 1, "col"],
#     red.f = 0.5, green.f = 0.5, blue.f = 0.5
#   ),
#   1, 7
# )
# 
# # Evaluate
# pt.test <- cv_range(
#   range_object = range.tiger,
#   cv = "block-cv",
#   nfolds = 5,
#   nblocks = 2
# )
# 
# # Use extent and country borders
# world.local.ti <- terra::crop(countries, ext.temp.t2)
# world.local.ti <- terra::aggregate(world.local.ti)
# pres <- all.xy[all.xy$Pres %in% 1, ]
# abs.pts <- all.xy[all.xy$Pres %in% 0, ]
# pres_coords <- as.data.frame(
#   pres[, c("decimalLongitude", "decimalLatitude")]
# )
# id.in <- terra::extract(range.tiger$rangeOutput, pres_coords)
# pres <- pres[!is.na(id.in[, 2]), ]
# 
# # Plot
# terra::plot(
#   world.local.ti, col = "#bcd1bc",
#   axes = FALSE, lwd = 1
# )
# graphics::points(
#   abs.pts, col = paste0(abs.pts$col, "50"),
#   pch = 16, cex = 0.6
# )
# terra::plot(
#   merge_range(range.tiger),
#   border = "black", lwd = 2,
#   col = "#63636370", add = TRUE
# )
# graphics::points(
#   pres, col = paste0(pres$col, "80"),
#   pch = 16, cex = 1.6
# )
# 
# # Evaluation text anchored to the tiger extent's top-left corner
# txt.x.t <- ext.temp.t2[1] + 0.02 * (ext.temp.t2[2] - ext.temp.t2[1])
# txt.y1.t <- ext.temp.t2[4] - 0.05 * (ext.temp.t2[4] - ext.temp.t2[3])
# txt.y2.t <- ext.temp.t2[4] - 0.10 * (ext.temp.t2[4] - ext.temp.t2[3])
# 
# graphics::text(
#   txt.x.t, txt.y1.t,
#   paste(
#     "Mean TSS =",
#     round(tail(pt.test[, "TSS"], 1), 2)
#   ),
#   cex = 1.5, font = 2, adj = 0
# )
# graphics::text(
#   txt.x.t, txt.y2.t,
#   paste(
#     "Mean Precision =",
#     round(tail(pt.test[, "Precision"], 1), 2)
#   ),
#   cex = 1.5, font = 2, adj = 0
# )

## ----fig-2b, echo = FALSE, out.width = "50%"----------------------------------
knitr::include_graphics("../man/figures/Fig2b_tiger_cv.png")

## ----fig2c, eval = FALSE------------------------------------------------------
# # -------------------------------------
# # Arctostaphylos alpinus (custom ecoregion)
# # -------------------------------------
# 
# root.dir <- ext_file()
# 
# res5km <- evaluate_range(
#   root_dir = root.dir,
#   valData_dir = "SDM",
#   ecoRM_dir = "EcoRM",
#   verbose = TRUE,
#   print_map = FALSE,
#   valData_type = "TIFF",
#   mask = NULL,
#   res_fact = NULL
# )
# 
# # Plot plant
# terra::plot(
#   contExtL.shp, col = "#dce8dc",
#   axes = FALSE, lwd = 1
# )
# terra::plot(
#   merge_range(range.arctoS),
#   lwd = 0.1, col = "#63636350", add = TRUE
# )
# terra::plot(
#   res5km$overlay_list[[1]],
#   col = c(
#     "#d1c845", "#e39c59", "#8d60cc", "#6cba6c"
#   ),
#   breaks = c(-0.5, 0.5, 1.5, 2.5, 3.5),
#   axes = FALSE, legend = FALSE,
#   las = 1, add = TRUE
# )
# 
# # Evaluation text anchored to the L extent's bottom-left corner
# ext.L <- terra::ext(contExtL.shp)
# txt.x <- ext.L[1] + 0.02 * (ext.L[2] - ext.L[1])
# txt.y1 <- ext.L[3] + 0.09 * (ext.L[4] - ext.L[3])
# txt.y2 <- ext.L[3] + 0.04 * (ext.L[4] - ext.L[3])
# 
# graphics::text(
#   txt.x, txt.y1,
#   paste(
#     "Mean TSS =",
#     round(res5km$df_eval[1, "TSS_ecoRM"], 2)
#   ),
#   cex = 1, font = 2, adj = 0
# )
# graphics::text(
#   txt.x, txt.y2,
#   paste(
#     "Mean Precision =",
#     round(res5km$df_eval[1, "Prec_ecoRM"], 2)
#   ),
#   cex = 1, font = 2, adj = 0
# )
# terra::plot(
#   contExtL.shp,
#   border = "#383d38", axes = FALSE,
#   lwd = 1, add = TRUE
# )

## ----fig-2c, echo = FALSE, out.width = "50%"----------------------------------
knitr::include_graphics("../man/figures/Fig2c_arcto_eval.png")

## ----fig2d, eval = FALSE------------------------------------------------------
# # -------------------------------------
# # Panthera tigris (packaged ecoregion)
# # -------------------------------------
# 
# # Plot tiger
# terra::plot(
#   world.local.ti, col = "#dce8dc",
#   axes = FALSE, lwd = 1
# )
# toPlot <- terra::mask(
#   res5km$overlay_list[[6]], world.local.ti
# )
# terra::plot(
#   toPlot,
#   col = c(
#     "#d1c845", "#e39c59", "#8d60cc", "#6cba6c"
#   ),
#   breaks = c(-0.5, 0.5, 1.5, 2.5, 3.5),
#   axes = FALSE, legend = FALSE,
#   las = 1, add = TRUE
# )
# 
# # Evaluation text anchored to the tiger extent's top-left corner
# txt.x.t <- ext.temp.t2[1] + 0.02 * (ext.temp.t2[2] - ext.temp.t2[1])
# txt.y1.t <- ext.temp.t2[4] - 0.05 * (ext.temp.t2[4] - ext.temp.t2[3])
# txt.y2.t <- ext.temp.t2[4] - 0.10 * (ext.temp.t2[4] - ext.temp.t2[3])
# 
# graphics::text(
#   txt.x.t, txt.y1.t,
#   paste(
#     "Mean TSS =",
#     round(res5km$df_eval[6, "TSS_ecoRM"], 2)
#   ),
#   cex = 1.5, font = 2, adj = 0
# )
# graphics::text(
#   txt.x.t, txt.y2.t,
#   paste(
#     "Mean Precision =",
#     round(res5km$df_eval[6, "Prec_ecoRM"], 2)
#   ),
#   cex = 1.5, font = 2, adj = 0
# )
# terra::plot(
#   world.local.ti,
#   border = "#383d38", axes = FALSE,
#   lwd = 1, add = TRUE
# )
# leg.x <- ext.temp.t2[1] + 0.05 * (ext.temp.t2[2] - ext.temp.t2[1])
# leg.y <- ext.temp.t2[3] + 0.20 * (ext.temp.t2[4] - ext.temp.t2[3])
# graphics::legend(
#   leg.x, leg.y,
#   legend = c(
#     "True Presences", "True Absences",
#     "False Presences", "False Absences"
#   ),
#   fill = c(
#     "#6cba6c", "#d1c845", "#e39c59", "#8d60cc"
#   ),
#   bg = NA, box.col = NA,
#   cex = 1.1,
#   x.intersp = 0.2
# )

## ----fig-2d, echo = FALSE, out.width = "50%"----------------------------------
knitr::include_graphics("../man/figures/Fig2d_tiger_eval.png")

## ----fig3a, eval = FALSE------------------------------------------------------
# # -------------------------------------
# # World maps: IUCN vs gbif.range richness
# # -------------------------------------
# 
# # CRS
# robin <- paste(
#   "+proj=robin +lon_0=0 +x_0=0 +y_0=0",
#   "+datum=WGS84 +units=m +no_defs +type=crs"
# )
# 
# # Reproject countries
# countries.robin <- terra::project(countries, robin)
# 
# # Boundary box
# bb <- terra::as.polygons(
#   terra::ext(-180, 180, -90, 90),
#   crs = "EPSG:4326"
# )
# bb <- terra::densify(bb, interval = 1)
# bb.robin <- terra::project(bb, robin)
# 
# # Shared diverging color palette for both maps
# col <- grDevices::colorRampPalette(
#   c("#67a9cf", "#f7f7f7", "#ef8a62")
# )
# 
# # Plot IUCN map
# max.iucn <- round(terra::minmax(iucn.robin)[2])
# terra::plot(
#   iucn.robin,
#   axes = FALSE, legend = FALSE,
#   col = col(10), smooth = TRUE,
#   mar = c(1, 1, 1, 5)
# )
# terra::plot(countries.robin, add = TRUE, lwd = 2)
# terra::plot(bb.robin, add = TRUE, lwd = 3)
# oldpar <- graphics::par(xpd = NA, lwd = 3)
# cscl(
#   colors = col(10),
#   crds = c(-9501111, 9734033, -11800000, -13000000),
#   zrng = c(0, max.iucn),
#   tickle = -0.3, cx = 1.1, lablag = -1.3,
#   tria = "b", at = seq(0, max.iucn, 10),
#   horiz = TRUE, title = "IUCN richness",
#   labs = seq(0, max.iucn, 10), titlag = 2
# )
# graphics::par(oldpar)
# 
# # Plot GBIF.RANGE map
# max.gf <- round(terra::minmax(gf.robin)[2])
# terra::plot(
#   gf.robin,
#   axes = FALSE, legend = FALSE,
#   col = col(10), smooth = TRUE,
#   mar = c(1, 1, 1, 5)
# )
# terra::plot(countries.robin, add = TRUE, lwd = 2)
# terra::plot(bb.robin, add = TRUE, lwd = 3)
# oldpar <- graphics::par(xpd = NA, lwd = 3)
# cscl(
#   colors = col(10),
#   crds = c(-9501111, 9734033, -11800000, -13000000),
#   zrng = c(0, max.gf),
#   tickle = -0.3, cx = 1.1, lablag = -1.3,
#   tria = "b", at = seq(0, max.gf, 10),
#   horiz = TRUE, title = "gbif.range richness",
#   labs = seq(0, max.gf, 10), titlag = 2
# )
# graphics::par(oldpar)

## ----fig-3-maps, echo = FALSE, out.width = "50%", fig.show = "hold"-----------
knitr::include_graphics(c(
  "../man/figures/Fig3_iucn.png",
  "../man/figures/Fig3_gbifrange.png"
))

## ----fig3b, eval = FALSE------------------------------------------------------
# # -------------------------------------
# # Scatter plots: richness and area agreement
# # -------------------------------------
# 
# # Load area table
# data(area_data)
# 
# # Extract data to plot
# cor.ras <- terra::rast(list(gf.robin, iucn.robin))
# names(cor.ras) <- c("RANGE", "IUCN")
# set.seed(1)
# samp.div <- terra::spatSample(
#   cor.ras, 5000, replace = FALSE, na.rm = TRUE
# )
# dat.plot <- list(samp.div, area_data[, -1])
# strings <- c("richness", "areas")
# 
# # Plot diversities relationship, side by side
# oldpar <- graphics::par(mfrow = c(1, 2), mar = c(5, 5.5, 4, 2))
# lapply(seq_along(dat.plot), function(x) {
# 
#   # Extract the data
#   xy <- dat.plot[[x]]
#   sex <- 2.5
#   col <- "#d95f0240"
#   add <- ""
# 
#   # Log or not depending on the output
#   if (strings[x] == "areas") {
#     xy <- log(xy + 1)
#     sex <- 2.5
#     col <- "#d95f0240"
#     add <- "(log)"
#   }
# 
#   # Plot points
#   graphics::plot(
#     xy[, 2], xy[, 1],
#     cex.axis = 1.7, cex = sex, col = col,
#     xlim = c(min(xy[, 2]), max(xy[, 2])),
#     ylim = c(min(xy[, 1]), max(xy[, 1])),
#     pch = 16,
#     xlab = paste("IUCN", strings[x], add),
#     ylab = paste("gbif.range", strings[x], add),
#     cex.lab = 1.7, font.lab = 1
#   )
# 
#   # Run Linear Models and spearman's correlation
#   lm.div <- stats::lm(xy[, 1] ~ xy[, 2])
# 
#   # Lin's concordance correlation coefficient
#   # (population covariance/variance)
#   ccc_lin <- function(x, y) {
#     mx <- mean(x)
#     my <- mean(y)
#     vx <- mean((x - mx)^2)
#     vy <- mean((y - my)^2)
#     sxy <- mean((x - mx) * (y - my))
#     2 * sxy / (vx + vy + (mx - my)^2)
#   }
#   ccc.div <- ccc_lin(xy[, 2], xy[, 1])
#   cor.div <- stats::cor(xy[, 2], xy[, 1])
#   adj.r2 <- summary(lm.div)[[9]]
# 
#   # Plot text
#   text_cor1 <- bquote("ccc" == .(round(ccc.div, 2)))
#   text_cor2 <- bquote("r" == .(round(cor.div, 2)))
#   fig_label(
#     text_cor1,
#     region = "plot", pos = "topleft",
#     bty = "n", font = 2, col = "#121212",
#     cex = 2, margin = 0.02
#   )
#   fig_label(
#     text_cor2,
#     region = "plot", pos = "bottomright",
#     bty = "n", font = 2, col = "#6f69c2",
#     cex = 2, margin = 0.02
#   )
# 
#   # Plot relationship
#   graphics::lines(
#     xy[, 2], lm.div$fit, lwd = 7, col = "#7570b3"
#   )
#   graphics::abline(
#     a = 0, b = 1, col = "#252525",
#     lwd = 5, lty = 2
#   )
# })
# graphics::par(oldpar)

## ----fig-3-scatter, echo = FALSE, out.width = "80%", fig.show = "hold"--------
knitr::include_graphics(c(
  "../man/figures/Fig3_scatterplots.png"
))

