Example 4: Cumulative Observations Across All Taxonomic Groups

Note: This example is part of the Global Sampling Effort Dataset repository, which provides pre-computed, taxon-stratified rasters of spatial sampling effort derived from GBIF occurrence records. For an overview of all available examples, taxonomic groups, etc., see the main page. If you use these data or code, please cite:

El-Gabbas, A. (2026) A global, taxon-stratified, high-resolution sampling-effort dataset from GBIF for bias-aware ecological modelling. Diversity and Distributions 32, no. 5: e70205. https://doi.org/10.1111/ddi.70205..

---

Setup: Load required packages and define plot theme

require(ecokit)
require(dplyr)
require(terra)
require(rworldmap)
require(colorRamps)
require(sf)
require(ggplot2)
require(grid)
require(tidyterra)

global_map <- sf::st_as_sf(rworldmap::getMap(resolution = "high"))

common_theme <- ggplot2::theme_void() + 
  ggplot2::theme(
    plot.margin = grid::unit(c(0, 0, 0, 0), "lines"),
    legend.position = "right",
    legend.box.spacing = grid::unit(10, "pt"),
    legend.margin = ggplot2::margin(),
    legend.title = ggplot2::element_text(size = 7),
    legend.text = ggplot2::element_text(size = 7))

---

Cumulative number of observations (5 km resolution; 1980–2025)

This example retrieves the cumulative number of observations across all taxonomic groups at 5 km resolution, aggregated over the full time period (1980–2025). The file is saved to the effort_maps directory.

This is the observation-count counterpart of Example 3, which shows species counts instead.

effort_all <- ecokit::get_sampling_effort( 
  group = "all", descendants = "all", metric = "n_obs", 
  years = "total", resolution = 5, out_dir = "effort_maps")

---

Visualise the raster

Load and transform the raster

Load the downloaded raster, replace zeros with NA (to avoid -Inf after transformation), and apply a log10 transformation for visualisation:

r_map <- terra::rast(effort_all$local_path[[1]]) %>% 
  terra::classify(cbind(0, NA)) %>% 
  log10()

Global map (log10 scale)

The map below shows the cumulative number of observations per 5 km grid cell, illustrating geographic variation in sampling intensity.

ggplot2::ggplot() +
  ggplot2::geom_sf(data = global_map, color = "black", size = 0.25, fill = "grey95") +
  tidyterra::geom_spatraster(data = r_map, maxcell = 2.3e6) +
  ggplot2::scale_fill_gradientn(colours = colorRamps::matlab.like2(100), na.value = "transparent") +
  ggplot2::geom_sf(data = global_map, color = "grey60", size = 0.125, fill = "transparent") +
  ggplot2::labs(fill = "# observations\n(log10)") +
  ggplot2::coord_sf(expand = FALSE) +
  common_theme

Europe (log10 scale)

r_map2 <- terra::crop(r_map, terra::ext(-11, 37.5, 35, 71))
ggplot2::ggplot() +
  tidyterra::geom_spatraster(data = r_map2, maxcell = 2.3e6) +
  ggplot2::scale_fill_gradientn(colours = colorRamps::matlab.like2(100), na.value = "transparent") +
  ggplot2::geom_sf(data = global_map, color = "grey30", size = 0.125, fill = "transparent") +
  ggplot2::labs(fill = "# observations\n(log10)") +
  ggplot2::coord_sf(expand = FALSE, xlim = c(-11, 37.5), ylim = c(35, 71)) +
  common_theme

USA (log10 scale)

r_map2 <- terra::crop(r_map, terra::ext(-125, -66.5, 24.5, 49.5))
ggplot2::ggplot() +
  tidyterra::geom_spatraster(data = r_map2, maxcell = 2.3e6) +
  ggplot2::scale_fill_gradientn(colours = colorRamps::matlab.like2(100), na.value = "transparent") +
  ggplot2::geom_sf(data = global_map, color = "grey30", size = 0.5, fill = "transparent") +
  ggplot2::labs(fill = "# observations\n(log10)") +
  ggplot2::coord_sf(expand = FALSE, xlim = c(-125, -66.5), ylim = c(24.5, 49.5)) +
  common_theme

India (log10 scale)

r_map2 <- terra::crop(r_map, terra::ext(68.1, 97.4, 6.7, 35.5))
ggplot2::ggplot() +
  tidyterra::geom_spatraster(data = r_map2, maxcell = 2.3e6) +
  ggplot2::scale_fill_gradientn(colours = colorRamps::matlab.like2(100), na.value = "transparent") +
  ggplot2::geom_sf(data = global_map, color = "grey30", size = 0.5, fill = "transparent") +
  ggplot2::labs(fill = "# observations\n(log10)") +
  ggplot2::coord_sf(expand = FALSE, xlim = c(68.1, 97.4), ylim = c(6.7, 35.5)) +
  common_theme

---

← Previous: Example 3 | Next: Example 5 →