Example 2: Total Number of Bird Observations (10 km Resolution)

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..

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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))

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Total number of bird observations globally (1980–2025; 10 km resolution)

The following call downloads a raster of the total number of bird observations globally (1980–2025) at 10 km resolution. The file is saved to the effort_maps directory.

effort_birds <- ecokit::get_sampling_effort( 
  group = "aves", descendants = "all", metric = "n_obs", 
  years = "total", resolution = 10, out_dir = "effort_maps")

Inspect the returned tibble

The returned tibble contains metadata columns (group, descendants, years, metric, resolution), the OSF file identifier, and the local path to the downloaded GeoTIFF.

dplyr::glimpse(effort_birds)

Rows: 1
Columns: 9
$ group      <chr> "aves"
$ descendant <chr> "all"
$ year       <chr> "total"
$ metric     <chr> "n_obs"
$ resolution <dbl> 10
$ name       <chr> "n_obs_Aves_res_10.tif"
$ id         <chr> "6914140ee039eec8340c1ff9"
$ local_path <chr> "effort_maps/n_obs_Aves_res_10.tif"
$ meta       <list> [[<NULL>, <NULL>, "n_obs_Aves_res_10.tif", "file", "/691414…

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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_birds$local_path[[1]]) %>% 
  terra::classify(cbind(0, NA)) %>% 
  log10()

The loaded raster can be visualised using standard terra, ggplot2, or other spatial workflows. The maps below illustrate the global and regional patterns.

Compare this example with Example 1, which shows recorded species counts rather than observation counts, and Example 5, which shows bird observations at 5 km resolution for 2015–2024 only.

Global map (log10 scale)

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

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