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The goal of gcube is to provide a simulation framework for biodiversity data cubes using the R programming language. This can start from simulating multiple species distributed in a landscape over a temporal scope. In a second phase, the simulation of a variety of observation processes and effort can generate actual occurrence datasets. Based on their (simulated) spatial uncertainty, occurrences can then be designated to a grid to form a data cube.

Simulation studies offer numerous benefits due to their ability to mimic real-world scenarios in controlled and customizable environments. Ecosystems and biodiversity data are very complex and involve a multitude of interacting factors. Simulations allow researchers to model and understand the complexity of ecological systems by varying parameters such as spatial and/or temporal clustering, species prevalence, etc.


You can install the development version from GitHub with:

# install.packages("remotes")

Package name rationale

The name gcube stands for ‘generate cube’ since it can be used to generate biodiversity data cubes from minimal input.


This is a basic example which shows you the workflow for simulating a biodiversity data cube. This is divided in three steps or processes:

  1. Occurrence process
  2. Detection process
  3. Grid designation process

The functions are set up such that a single polygon as input is enough to go through this workflow using default arguments. The user can change these arguments to allow for more flexibility.

# Load packages

library(sf)      # working with spatial objects
library(dplyr)   # data wrangling
library(ggplot2) # visualisation with ggplot

We create a random polygon as input.

# Create a polygon to simulate occurrences
polygon <- st_polygon(list(cbind(c(5, 10, 8, 2, 3, 5), c(2, 1, 7,9, 5, 2))))

# Visualise
ggplot() + 
  geom_sf(data = polygon) +

Spatial extend in which we will simulate species occurrences.

Occurrence process

We generate occurrence points within the polygon using the simulate_occurrences() function. These are the “real” occurrences of the species, whether we have observed them or not. In the simulate_occurrences() function, the user can specify different levels of spatial clustering, and can define the trend change of the species over time.

# Simulate occurrences within polygon
occurrences_df <- simulate_occurrences(
  plgn = polygon,
  seed = 123)
#> [using unconditional Gaussian simulation]

# Visualise
ggplot() + 
  geom_sf(data = polygon) +
  geom_sf(data = occurrences_df) +

Spatial distribution of occurrences within the polygon.

Detection process

In this step we define the sampling process, based on the detection probability of the species and the sampling bias. This is done using the sample_observations() function. The default sampling bias is "no_bias", but bias can also be inserted using a polygon or a grid.

# Detect occurrences
detections_df_raw <- sample_observations(
  occurrences = occurrences_df,
  detection_probability = 0.5,
  seed = 123)

# Visualise
ggplot() + 
  geom_sf(data = polygon) +
  geom_sf(data = detections_df_raw,
          aes(colour = sampling_status)) +

Spatial distribution of occurrences with indication of sampling status.

We select the detected occurrences and add an uncertainty to these observations. This can be done using the filter_observations() and add_coordinate_uncertainty() functions, respectively.

# Select detected occurrences only
detections_df <- filter_observations(
  observations_total = detections_df_raw)

# Add coordinate uncertainty
coord_uncertainty_vec <- rgamma(nrow(detections_df), shape = 2, rate = 6)
observations_df <- add_coordinate_uncertainty(
  observations = detections_df,
  coords_uncertainty_meters = coord_uncertainty_vec)

# Created and sf object with uncertainty circles to visualise
buffered_observations <- st_buffer(

# Visualise
ggplot() + 
  geom_sf(data = polygon) +
  geom_sf(data = buffered_observations,
          fill = alpha("firebrick", 0.3)) +
  geom_sf(data = observations_df, colour = "firebrick") +

Spatial distribution of detected occurrences with coordinate uncertainty.

Grid designation process

Finally, observations are designated to a grid to create an occurrence cube. We create a grid over the spatial extend using sf::st_make_grid().

# Define a grid over spatial extend
grid_df <- st_make_grid(
    square = TRUE,
    cellsize = c(1.2, 1.2)
  ) %>%
  st_sf() %>%
  mutate(intersect = as.vector(st_intersects(geometry, polygon,
                                             sparse = FALSE))) %>%
  dplyr::filter(intersect == TRUE) %>%

To create an occurrence cube, grid_designation() will randomly take a point within the uncertainty circle around the observations. These points can be extracted by setting the argument aggregate = FALSE.

# Create occurrence cube
occurrence_cube_df <- grid_designation(
  observations = observations_df,
  grid = grid_df,
  seed = 123)

# Get sampled points within uncertainty circle
sampled_points <- grid_designation(
  observations = observations_df,
  grid = grid_df,
  aggregate = FALSE,
  seed = 123)

# Visualise grid designation
ggplot() +
  geom_sf(data = occurrence_cube_df, linewidth = 1) +
  geom_sf_text(data = occurrence_cube_df, aes(label = n)) +
  geom_sf(data = buffered_observations,
          fill = alpha("firebrick", 0.3)) +
  geom_sf(data = sampled_points, colour = "blue") +
  geom_sf(data = observations_df, colour = "firebrick") +
  labs(x = "", y = "", fill = "n") +

Distribution of random samples within uncertainty circle.

The output gives the number of observations per grid cell and minimal coordinate uncertainty per grid cell.

# Visualise minimal coordinate uncertainty
ggplot() +
  geom_sf(data = occurrence_cube_df, aes(fill = min_coord_uncertainty),
          alpha = 0.5, linewidth = 1) +
  geom_sf_text(data = occurrence_cube_df, aes(label = n)) +
  scale_fill_continuous(type = "viridis") +
  labs(x = "", y = "") +

Distribution of minimal coordinate uncertainty.